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Dermatopathology and associated laboratory investigations in the study of skin disease.

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The diagnosis of skin disease requires both clinical and pathological expertise, which subsequently define the laboratory tests required. Central to these investigations is the biopsy, allowing key architectural features of the skin disease to be analysed, emphasizing the importance of dermatopathology to the ...

The diagnosis of skin disease requires both clinical and pathological expertise, which subsequently define the laboratory tests required. Central to these investigations is the biopsy, allowing key architectural features of the skin disease to be analysed, emphasizing the importance of dermatopathology to the treatment of skin disorders. In addition, and frequently of vital importance, is a range of laboratory procedures and tests that sit outside the realms of routine dermatopathology and which assist in the diagnosis and aid in the patient management of skin disease. This special issue of the journal will include a number of review papers, original articles and short innovation communications. These articles will explore why dermatopathology is often key in the diagnosis of skin disease and also explain its relationship and important links to other specialised laboratory services that help define and classify cutaneous disorders. Topics will include: - Immunodermatology - Mycosis - Molecular diagnostic methods - Innovations in dermatopathology - Unusual applications of Mohs micrographic surgery - Predictive and prognostic markers of melanoma - Immune checkpoint inhibition in melanomas - Dermatopathology in alopecia. We look forward to receiving manuscript submissions on these areas, and other related topics. All manuscripts are published once fully reviewed, ensuring your work is accessible as soon as possible.

Keywords : dermatopathology, immunodermatology, mycosis, histology, skin disease, Mohs surgery, melanoma, alopecia

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• Published: 24 November 2021

Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

• Bin Zhang 1 , 2 ,
• Xue Zhou 1 , 2 ,
• Yichen Luo 1 , 2 ,
• Hao Zhang 1 , 2 ,
• Huayong Yang 1 , 2 ,
• Jien Ma 3 &
• Liang Ma   ORCID: orcid.org/0000-0002-6242-1850 1 , 2  

Chinese Journal of Mechanical Engineering volume  34 , Article number:  112 ( 2021 ) Cite this article

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Deep learning has become an extremely popular method in recent years, and can be a powerful tool in complex, prior-knowledge-required areas, especially in the field of biomedicine, which is now facing the problem of inadequate medical resources. The application of deep learning in disease diagnosis has become a new research topic in dermatology. This paper aims to provide a quick review of the classification of skin disease using deep learning to summarize the characteristics of skin lesions and the status of image technology. We study the characteristics of skin disease and review the research on skin disease classification using deep learning. We analyze these studies using datasets, data processing, classification models, and evaluation criteria. We summarize the development of this field, illustrate the key steps and influencing factors of dermatological diagnosis, and identify the challenges and opportunities at this stage. Our research confirms that a skin disease recognition method based on deep learning can be superior to professional dermatologists in specific scenarios and has broad research prospects.

1 Introduction

Skin lesions are a common disease that cause suffering, some of which can have serious consequences, for millions of people globally [ 1 ]. Because of its complexity, diversity, and similarity, skin disease can only be diagnosed by dermatologists with long-term clinical experience and is rarely reproducible. It is likely to be misdiagnosed by an inexperienced dermatologist, which can exacerbate the condition and impede appropriate treatment. Thus, it is necessary to provide a quick and reliable method to assist patients and dermatologists in data processing and judgment.

Advances in deep learning have influenced numerous scientific and industrial fields and have realized significant achievements with inspiration from the human nervous system. With the rapid development of deep learning in biomedical data processing, numerous specialists have adopted this technique to acquire more precise and accurate data. With the rapid increase in the amount of available biomedical data including images, medical records, and omics, deep learning has achieved considerable success in a number of medical image processing problems [ 2 , 3 , 4 ]. In this regard, deep learning is expected to influence the roles of image experts in biomedical diagnosis owing to its ability to perform quick and accurate assessments. This paper presents the characteristics of skin lesions, overviews image techniques, generalizes the developments in deep learning for skin disease classification, and discusses the limitations and direction of automatic diagnosis.

2 Features of Skin Disease

The skin is the largest organ of the human body; in adults, it can typically weigh 3.6 kg and cover 2 m 2 [ 5 ]. Skin guards the body against extremes of temperature, damaging sunlight, and harmful chemicals. As a highly organized structure, it consists of the epidermis, dermis, and hypodermis, providing the functions of protection, sensation, and thermoregulation [ 6 ]. The epidermis, the outermost layer of the skin, provides an excellent aegis to avoid environmental aggression. The dermis, beneath the epidermis, contains tough connective tissue, hair follicles, and sweat glands, which leads to the differentiation of skin appearance [ 7 ]. There are numerous causes of skin disease, including physical factors such as light, temperature, and friction, and biological factors such as insect bites, allergic diseases, and even viral infections. Environmental and genetic factors can also lead to the occurrence of skin diseases. In lesion imaging, complicating difficulties can include variations in skin tone, presence of artifacts such as hair, air bubbles, non-uniform lighting, and the physical location of the lesion. Moreover, the majority of lesions vary in terms of color, texture, shape, size, and location in an image frame [ 8 ]. There are 5.4 million new skin cancer patients in America every year. As of 2014, there were 420 million people globally suffering from skin disease, including nearly 150 million people in China, the population of which accounts for 22% of the world’s population, yet medical resources account for only 2%. Influenced by the living environment, areas with reduced economic development and poverty are more prone to skin disease. The high cost of treatment, repeated illness occurrences, and delays in treatment have focused attention on the requirement for healthy survival and social development. The high cost of treatment, repeated illness occurrences, and delays in treatment have brought challenges to the healthy survival and social development.

The accurate diagnosis of a particular skin disease can be a challenging task, mainly for the following reasons. First, there are numerous kinds of dermatoses, nearly 3000 recorded in the literature. Stanford University has developed an algorithm to demonstrate generalizable classification with a new dermatologist-labeled dataset of 129450 clinical images divided into 2032 categories [ 9 ]. Figure 1 displays a subset of the full taxonomy; this has been organized clinically and visually by medical experts. Secondly, the complex manifestation of the disease is also a major challenge for doctors. Morphological differences in the appearance of skin lesions directly influence the diagnosis mainly as there can be relatively poor contrast between different skin diseases, which cannot be distinguished without considerable experience. Finally, for different skin diseases, the lesions can be overly similar to be distinguished using only visual information. Different diseases can have similar manifestations and the same disease can have different manifestations in different people, body parts, and disease periods [ 10 ]. Figure 2 displays sample images demonstrating the difficulty in distinguishing between malignant and benign lesions, which share several visual features. Unlike benign skin diseases, malignant diseases, if not treated promptly, can lead serious consequences. Melanoma [ 11 ], for example, is one of the major and most fatal skin cancers. The five-year survival rate of melanoma can be greater than 98% if found in time; this figure in those where spread has occurred demonstrates a significant drop to 17% [ 12 ]. In 2015, there were 3.1 million active cases, representing approximately 70% of skin cancer deaths worldwide [ 13 , 14 ].

Reproduced with permission from Ref. [ 9 ] and credit (CC BY 4.0))

Subset of top of tree-structured taxonomy of skin disease (

Malignant and benign sample images from two disease classes (

The diagnosis of skin disease relies on clinical experience and visual perception. However, human visual diagnosis is subjective and lacks accuracy and repeatability, which is not found in computerized skin-image analysis systems. The use of these systems enables inexperienced operators to prescreen patients [ 15 ]. Compared with other diseases or applications such as industrial fault diagnosis, the visual manifestation of skin disease is more prominent, facilitating the significant value of deep learning in image recognition with visual sensitivity. Through the study of large detailed images, dermatology can become one of the most suitable medical fields for telemedicine and artificial intelligence (AI). Using imaging methods, it could be possible for deep learning to assist or even replace dermatologists in the diagnosis of skin disease in the near future.

3 Image Methods

Deep learning is a class of machine learning that automatically learns hierarchical features of data using multiple layers composed of simple and nonlinear modules. It transforms the data into representations that are important for discriminating the data [ 16 ]. As early as 1998, the LeNet network was proposed for handwritten digital recognition [ 17 ]. However, owing to the lack of computational power, it was difficult to support the required computation. Until 2012, this method was successfully applied and overwhelmingly outperformed previous machine learning methods for visual recognition tasks at a competitive challenge in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [ 18 , 19 ]. This was a breakthrough that used convolutional networks to virtually halve the error rate for object recognition, and precipitated the rapid adoption of deep learning by the computer vision community [ 16 ]. Since then, deep learning algorithms have undergone considerable development because of the improved capabilities of hardware such as graphics processing units (GPUs). Different models, such as ZFNet [ 20 ], VGG [ 21 ], GoogLeNet [ 22 ], and ResNet [ 23 ], have been proposed. The top-5 error rate in ImageNet dropped from 16.4% in 2012 to 2.25% in 2017 (Figure 3 ); correspondingly, that of humans was approximately 5%. It has dramatically improved tasks in different scientific and industrial fields including not only computer vision but also speech recognition, drug discovery, clinical surgery, and bioinformatics [ 24 , 25 , 26 ].

Deeper networks, lower error rates

The structure of a convolutional neural network (CNN), which is a representative deep learning algorithm, is displayed in Figure 4 . The actual model is similar to this figure, in addition to deeper layers and more convolution kernels. A CNN is a type of “feedforward neural network” inspired by human visual perception mechanisms, and can learn a large number of mappings between inputs and outputs without any precise mathematical expression between them. The first convolutional filter of the CNN is used to detect low-order features such as edges, angles, and curves. As the convolutional layer increases, the detected features become more complex [ 20 ]. The pooling layer, or named subsampling layer, converts a window into a pixel by taking the maximum or average value [ 27 ], which can reduce the size of the feature map. After the image passes the last fully connected layer, the model maps the learned distributed feature to the sample mark space and provides the final classification type. The layout of the CNN is similar to the biological neural network, with sparse structures and shared weights, which can reduce the number of parameters and improve the fitting effect to prevent overfitting. Deep CNNs demonstrate the potential for variable tasks across numerous fine-grained object categories and have unique advantages in the field of image recognition.

CNN architecture and principles

The selection of a suitable model is crucial. The GoogLeNet model, with a structure called inception (Figure 5 ), is proposed which can not only maintain the sparsity of the network structure but can also use the high computational performance of the dense matrix [ 22 ]. GoogLeNet has been learned and used by numerous researchers because of its excellent performance. Therefore, the Google team has further explored and improved it, resulting in an upgraded version of GoogLeNet, Inception v3 [ 28 ], which has become the first choice for current research. With Google’s Inception v3 CNN architecture pretrained to a high-level accuracy on the 1000 object class of ImageNet, researchers can remove the final classification layer from the network, retrain it with their own dataset, and fine-tune the parameters across all the layers.

Inception module: ( a ) Inception module, naïve version; ( b ) Inception module with dimension reductions

Google’s TensorFlow [ 29 ], Caffe [ 30 ], and Theano [ 31 ] deep learning frameworks can be used for training. Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions. It pioneered the trend of using symbolic graphs for programming a network; however, it lacks a low-level interface and the inefficiency of the Python interpreter limits its usage. Caffe’s ConvNet implementation with numerous extensions being actively added is excellent; however, its support for recurrent networks and language modeling in general is poor. If both CPU and GPU supports are required, additional functions must be implemented. Specifying a new network is fairly easy in TensorFlow using a symbolic graph of vector operations; however, it has a major weakness in terms of modeling flexibility. It has a clean, modular architecture with multiple frontends and execution platforms, and the library can be compiled on Advanced RISC Machines (ARM).

Deep learning has been gradually applied to medical image data, as medical image analysis approaches are considerably similar to computer vision techniques [ 32 ]. Although numerous studies were initially undertaken using relatively small datasets and a pretrained deep learning model as a feasibility study, a robust validation of the medical application is required [ 33 , 34 , 35 ]. Hence, big data from medical images have been collected to validate the feasibility of medical applications [ 9 , 36 ]. For example, Google researchers collected large datasets consisting of more than 120,000 retinal fundus images for diagnosing diabetic retinopathy and demonstrated high sensitivity and specificity for detection [ 37 ].

Owing to the development of hardware and advancement of the algorithms, deep learning now includes considerably more functionality than could previously be imagined. Researchers are now more likely to predict and distinguish what is difficult to diagnose with complex mechanisms and similar characterizations [ 38 , 39 ]. Deep learning is a powerful machine learning algorithm for classification while extracting low- to high-level features [ 40 , 41 ]. A key difference in deep learning compared to other diagnostic methods is its self-learning nature. The neural network is not designed by humans; rather, it is designed by the data itself. Table 1 presents several published achievements on disease diagnosis using pictures or clinical images, which proves that deep learning can be compared with professional specialists in certain fields. Furthermore, many researchers have indicated interest in mobile diagnostics that allow the use of mobile technology. Smartphones with sufficient computing power and fast development to extend the versatility and utility could be used to scan, calculate, analyze anytime and anywhere to detect skin disease [ 42 , 43 , 44 ]. Researchers have developed such a system based on AI that allows users to install apps on their smartphones and analyze and judge suspicious lesions on the body by taking a picture [ 45 ].

4 Skin Disease Classification Using Deep Learning

Using the deep learning technique, the pattern recognition of images can be performed automatically once the program is established. Images can be input to a CNN with high fidelity and important features can be automatically obtained. Therefore, information extraction from images prior to the learning process is not necessary with this technique. In shallow layers, simple features such as the edges within the images are learned. At deep layers near the output layer, more complex high-order features are learned [ 56 ]. Different researchers, institutions, and challenges are working on the automatic diagnosis of skin disease, and different deep learning methods have been developed for the recognition of dermatological disease; these have been proven to be effective in numerous fields [ 57 ]. For example, the International Skin Imaging Collaboration (ISIC) is a challenge that focuses on the automatic analysis of skin lesions. The goal of the challenge (started in 2017) is to support the research and development of algorithms for the automated diagnosis of melanoma including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma [ 58 , 59 ], which is also the main goal in the field of dermatology [ 60 ]. In general, this method is a modeling framework that can learn the functional mapping from the input images to output. The input image is a preprocessed image; the output image is a segmentation mask. The network structure involves a series of convolution and pooling layers, followed by a fully connected layer, followed by a series of unpooling and disconnection operations [ 61 ].

The diagnosis of skin diseases typically consists of four components: image acquisition, image preprocessing, feature extraction and classification, and evaluation of the criteria. Image acquisition is the basis for skin classification, and more images typically indicate greater accuracy and better adaptability (for the data size of selected projects, please refer to Table 2 ). Preprocessing is used to crop and zoom the images and segment lesions for better training. Feature extraction mainly acquires the features of the skin lesions through color, texture, and boundary information. The evaluation of the results is the final step, which is used to judge whether the classification model is reasonable and achieves its objective.

4.1 Image Acquisition

Deep learning requires a large number of images to extract disease features. These datasets are typically available from the Internet, open dermatology databases, and hospitals in collaboration with research units, and are labeled by professional dermatologists after removing blurry and distant images. An excellent dataset should be composed of dermoscopic images. Dermoscopy is a non-invasive skin imaging technology that can observe the skin structure at the junction of the epidermis and dermis, and clearly indicate the nature, distribution, arrangement, edge, and shape of pigmented skin lesions. Because of the uncertainty of imaging conditions, such as shooting angle, illumination, and storage pixels, the imaging effect of non-dermoscopic images can be influenced. Selected published datasets are listed in Table 3 covering more than a dozen kind of skin diseases, among which melanoma has the greatest probability of occurrence. However, owing to the lack of a unified standard for skin disease images, the labeling of images is time-consuming and labor-intensive, which significantly limits the size of the current public datasets. Therefore, numerous studies have combined multiple datasets for use [ 43 , 63 ].

4.2 Image Preprocessing

Effective image quality can improve the generalization ability of a model. Preprocessing can reduce irrelevant information in the image, improve the intensity of the relevant information, simplify the data, and improve the reliability. The general image preprocessing process is as follows:

Image segmentation. Skin lesion segmentation is the essential step for the majority of classification tasks. Accurate segmentation contributes to the accuracy, computation time, and error rate of subsequent lesion classification [ 71 , 72 ]. It is crucial for image analysis for the following two reasons. First, the border of a lesion provides important information for accurate diagnosis, including numerous clinical features such as asymmetry and border irregularity. Secondly, the extraction of other important clinical features such as atypical dots and color variegation critically depends on the accuracy of the border detection [ 8 , 73 ]. Given a inputted dermoscopic image (Figure 6 a), the goal of the segmentation process is to generate a two-dimensional mask (Figure 6 b) that provides an accurate separation between the lesion area and surrounding healthy skin [ 74 ].

Resize. Lesions frequently occupy a relatively small area, although skin images can be considerably large [ 75 , 76 ]. Before this task, images for a deep learning network should be preprocessed because the resolution of the original lesion images is typically overly large, which entails a high computation cost [ 77 ]. Accurate skin lesion segmentation enhances its capability by incorporating a multiscale contextual information integration scheme [ 62 ]. To avoid distorting the shape of the skin lesion, the images should be cropped to the center area first and then proportionally resized. Images are frequently resized to 224×224 or 227×227 pixels through scaling and clipping [ 78 ], which is the appropriate size after combining the amount of calculation and information density.

Normalization. The image data are mapped to the interval of [0,1] or [−1,1] in the same dimension. The essence of normalization is a kind of linear transformation that does not cause “failure” after changing the data. Conversely, it can improve the performance of the data, accelerate the solution speed of gradient descent, and enhance the convergence speed of the model.

Data augmentation. Owing to privacy and professional equipment problems, it is difficult to collect sufficient data in the process of skin disease identification. A data set that is overly small can easily lead to overfitting owing to the lack of learning ability of the model, which makes the network model lack generalization ability. A method called data augmentation is adopted to expand the dataset to meet the requirements of deep learning for big data, such as rotation, random cropping, and noise [ 79 ]. Figure 7 displays several methods of image processing by which the image database can be extended to meet the training requirements.

Skin lesion segmentation: ( a ) Dermoscopic image input; ( b ) Binary mask output

Data argumentation: ( a ) Original image; ( b ) Flip; ( c ) Random crop; ( d ) Rotation; ( e ) Shift; ( f ) Color jittering; ( g ) Noise; ( h ) Standardization; and ( i ) Paste

4.3 Feature Extraction and Classification

Early detection of lesions is a crucial step in the field of skin cancer treatment. There is a significant benefit if this can be achieved without penetrating the body. Feature extraction of skin disease is an important tool that can be used to properly analyze and explore an image [ 80 ]. Feature extraction can be simply viewed as a dimensionality reduction process; that is, converting picture data into a vector of a certain dimension with picture features. Before deep learning, this was typically determined manually by dermatologists or researchers after investigating a large number of digital skin lesion images. A well-known method for feature extraction is based on the ABCD rule of dermoscopy. ABCD stands for asymmetry, border structure, color variation, and lesion diameter. It defines the basis for disease diagnosis [ 81 ]. The extracted and fused traits such as color, texture, and Histogram of Oriented Gradient (HOG) are applied subsequently with a serial-based method. The fused features are selected afterwards by implementing a novel Boltzman entropy method [ 82 ], which can be used for the early detection. However, this typically has enormous randomness and depends on the quantity and quality of the pictures, as well as the experience of the dermatologists.

From a classification perspective, feature extraction has numerous benefits: (i) reducing classifier complexity for better generalization, (ii) improving prediction accuracy, (iii) reducing training and testing time, and (iv) enhancing the understanding and visualization of the data. The mechanism of neural networks is considerably different from that of traditional methods. Visualization indicates that the first layers are essentially calculating edge gradients and other simple operations such as SIFT [ 83 ] and HOG [ 84 ]. The folded layers combine the local patterns into a more global pattern, ultimately resulting in a more powerful feature extractor. In a study using nearly 130000 clinical dermatology images, 21 certified dermatologists tested the skin lesion classification with a single CNN, directly using pixels and image labels for end-to-end training; this had an accuracy of 0.96 for carcinoma [ 9 ]. Subsequently, researchers used deep learning to develop an automated classification system for 12 skin disorders by learning the abnormal characteristics of a malignancy and determined visual explanations from the deep network [ 47 ]. A third study combined deep learning with traditional methods such as hand-coded feature extraction and sparse coding to create a collection for melanoma detection that could yield higher performance than expert dermatologists. These results and others [ 85 , 86 , 87 ] confirm that deep learning has significant potential to reduce doctors’ repetitive work. Despite problems, it would be a significant advance if AI could reliably simulate experienced dermatologists.

4.4 Evaluation Criteria and Benchmarking

Evaluation and criterion, typically based on the following three points, reliability, time consumption, and training and validation are vital in this field [ 88 ]. Researchers [ 73 , 89 , 90 ] have used all three criteria to develop and design methods and techniques for detecting and diagnosing skin disease. Others [ 71 , 91 , 92 ] have used only two criteria, reliability, and training and validation to evaluate and discuss the different types of classifiers.

Numerous studies have demonstrated that acceptable reliability, time complexity, and error rates within a dataset cannot be achieved at the same time; hence, researchers must establish different standards. Once one of them is selected, the performance of the others diminishes [ 90 , 93 ]. Consequently, conflicts among dermatological evaluation criteria pose a serious challenge to dermatological classification methods. These requirements must be considered during the evaluation and benchmarking. The dermatological classification method should standardize the requirements and objectives and use a programmatic process in research, evaluation, and benchmarking. Moreover, new flexible evaluations should address all conflicting standards and issues [ 94 ].

Despite the conflicts, important criteria are the key goals for evaluation and benchmarking. It is necessary to develop appropriate procedures for these goals while increasing the importance of specific evaluation criteria and decreasing other standards [ 95 ]. When evaluating the results obtained using the diagnostic model, researchers must consider the quality of the dataset used to build the model and choose the parameters that can adjust that model. The time complexity and error rate in the dataset have proven to be important in the field of dermatology, which, with more consideration during the evaluation process, can optimize the consistency of the results [ 63 ]. In general, the goal is to obtain a balanced classifier for sensitivity and specificity.

5 Limitations and Prospect

5.1 limitations.

In general, the advantage of AI is that it can help doctors perform tedious repetitive tasks. For example, if sufficient blood is scanned, an AI-powered microscope can detect low-density infections in micrographs of standard, field-prepared thick blood films, which is considered to be time-consuming, difficult, and tedious owing to the low density and small parasite size and abundance of similar non-parasite objects [ 49 ]. The requirement for staff training and purchase of expensive equipment for creating dermoscopic images can be replaced by software using CNNs [ 96 ]. In the future, the clinical application of deep learning for the diagnosis of other diseases can be investigated. Transfer learning could be useful in developing CNN models for relatively rare diseases. Models could also evolve such that they require fewer preprocessing steps. In addition to these topics, a deeper understanding of the reconstruction kernel or image thickness could lead to improved deep learning model performance. Positive effects should continue to grow owing to the emergence of higher precision scanners and image reconstruction techniques [ 56 ]. However, we must realize that although AI has the ability to defeat humans in several specific fields, in general the performance of AI is considerably less than acceptable in the majority of cases [ 97 ]. The main reasons for this are as follows.

Medicine is an area that is not yet fully understood. Information is not completely transparent. The characteristics of dermatology determine that the majority of the data cannot be obtained. At the same time, the AI technology route is immature, the identification accuracy of which must be improved owing to the uncertainty of manual diagnosis. There is no strict correspondence between the symptoms and results of a disease and no clear boundary between the different diseases. Thus, the use of deep learning for disease diagnosis continues to require considerable effort.

Before systematic debugging, extensive simulation, and robust validation, flawed algorithms could harm patients, which could lead to medical ethical issues, and therefore require forward-looking scrutiny and stricter regulation [ 98 ]. As a “black box”, the principle of deep learning is unexplained at this stage, which could result in unpredictable system output. Moreover, it is possible that humans could not truly understand how a machine functions, even though it is actually inspired by humans [ 99 , 100 ]. Hence, whether or not patient care can be accepted using an opaque algorithm remains a point of discussion.

There is a problem with the change in the error rate value in a dataset, which is caused by the change in the size of the dataset used in different skin cancer experiments. Therefore, the lack of a standard dataset can lead to serious problems; the error rate values are considered in many experiments. In addition, the collection of datasets for numerous studies depends on individual research, leading to unnecessary effort and time. When the actual class is manually marked and compared to the predicted class to calculate one of the parameter matrices, pixels are lost when the background is cut from the skin cancer image using Adobe Photoshop [ 101 ]. At this point, the process influences the results of all the parameter reliability groups (matrices, relationships, and behaviors), which are considered controversial. High reliability and low rate of time complexity cannot be achieved simultaneously, which is reflected in the training process and is influenced by conflicts between different standards, leading to considerable challenges [ 93 , 102 ]. A method that works for the detection of one skin lesion could possibly not work for the detection of others [ 103 ]. Numerous different training and test sets have been used to evaluate the proposed methods. Moreover, for the parameters in the training and evaluation, different researchers are interested in different parts. This lack of uniformity and standardization across all papers makes a fair comparison virtually impossible [ 50 , 104 ]. Although these indicators in the literature have been widely criticized, studies continue to use them to evaluate the application to skin cancer and other image processing fields.

The data used for evaluation are frequently overly small to allow a convincing statement regarding a system’s performance to be made. Although it is not impossible to collect an abundance of relevant data through the Internet in this information age, this information, with significant uncertainty, apparently cannot meet the requirements of independent and identical distribution, which is one of the important prerequisites for deep learning to be successfully applied. For certain rare diseases and minorities, only a limited number of images are available for training. To date, a large number of algorithms have demonstrated prejudice against minority groups, which could cause a greater gap in health service between the “haves” and the “have-nots” [ 105 ]. Numerous cases are required for the training process using deep learning techniques. In addition, although the deep learning technique has been successfully applied to other tasks, the developed models in skin are valid in only specific dedicated diseases and are not applicable to common situations. Diagnosing dermatology is a complex process that, in addition to image recognition, must be supplemented by other means such as palpation, smell, temperature change, and microscopy.

5.2 Prospect

Deep learning has made considerable progress in the field of skin disease recognition. More attempts and explorations in the future can be considered in the following aspects.

Establishment of standardized skin disease image dataset

A large amount of data is the basis of skin disease recognition and the premise of acceptable generalization ability of the network model. However, the number of images, disease types, image size, and shooting and processing methods of the published datasets are considerably different, which leads to the confusion of different studies and the loss of the ability to quantitatively describe different models, Moreover, it is difficult to collect images of certain rare diseases. As mentioned above, there are numerous kinds of skin diseases; however, only approximately 20 datasets are available, including less than 20 kinds of skin diseases. There is an urgent requirement to expand access to medical images. For example, Indian researchers have trained neural networks to analyze images from “handheld imaging devices” instead of stationary dermatoscope devices to provide more prospects for early and correct diagnosis [ 106 ]. However, a public database that allows the collection of a sufficient number of labeled datasets is likely necessary to truly represent projections of the population.

Interpretability of skin disease recognition

The progress of deep learning in skin disease recognition depends on a highly nonlinear model and parameter adjustment technology. However, the majority of the neural networks are “black box” models, and their internal decision-making process is difficult to understand. This “end-to-end” decision-making mode leads to the weak explanatory power of deep learning. The internal logic of deep learning is not clear, which makes the diagnosis results of the model less convincing. The interpretability research of skin disease classification could allow the owner of the system to clearly know the behavior and boundary of the system, and ensure the reliability and safety of the system. Moreover, it could monitor the moral problems and violations caused by training data deviation and provide a superior mechanism to follow the requirements within an organization to solve the bias and audit problems caused by AI [ 107 ].

Intelligent diagnosis and treatment of skin diseases

Deep learning can be used to address the increasing number of patients with skin disease and relieve the pressure of limited dermatologists. With the popularity of mobile phones, mobile computers, and wearable devices, a skin disease recognition system based on deep learning can be expected to be available to intelligent devices to serve more people. Using a mobile device camera, users can upload their own photos of the affected area to the cloud recognition system and download the diagnosis results at any time. Through simple communication with the “skin manager”, diagnosis suggestions and possible treatment methods could be available. Furthermore, the “skin manager” could monitor the user’s skin condition and provide real-time protection methods and treatment suggestions.

Computer diagnostic systems can assist trained dermatologists rather than replace them. These systems can also be useful for untrained general practitioners or telemedicine clinics. For health systems, improving workflows could increase efficiency and reduce medical errors. Hospitals could make use of large-scale data and recommend data sharing with a cloud-based platform, thus facilitating multihospital collaboration [ 108 ]. For patients, it should be possible to enjoy the medical resources of the top hospitals in big cities in remote and less modernized areas by telemedicine or enabling them to process their own data [ 109 ].

6 Conclusions

The potential benefits of deep learning solutions for skin disease are tremendous and there is an unparalleled advantage in reducing the repetitive work of dermatologists and pressure on medical resources. Accurate detection is a tedious task that inevitably increases the demand for a reliable automated detection process that can be adopted routinely in the diagnostic process by expert and non-expert clinicians. Deep learning is a comprehensive subject that requires a wide range of knowledge in engineering, information, computer science, and medicine. With the continuous development of the above fields, deep learning is undergoing rapid development and has attracted the attention of numerous countries. Powered by more affordable solutions, software that can quickly collect and meaningfully process massive data, and hardware that can accomplish what people cannot, it is evident that deep learning for the identification of skin disease is a potential technique in the foreseeable future.

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Supported by Key Research and Development Projects of Zhejiang Province of China (Grant No. 2017C01054), National Key Research and Development Program of China (Grant No. 2018YFA0703000), National Natural Science Foundation of China (Grant No. 51875518), and Fundamental Research Funds for the Central Universities of China (Grant Nos. 2019XZZX003-02, 2019FZA4002).

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Bin Zhang, Xue Zhou, Yichen Luo, Hao Zhang, Huayong Yang & Liang Ma

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BZ was in charge of the whole trial; XZ wrote the manuscript; YCL assisted with structure and language of the manuscript. HZ designed the experiments; HYY assisted with experimental setup; LM and JEM jointly supervised this work; All authors read and approved the final manuscript.

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Bin Zhang received his PhD degree in Engineering from Zhejiang University, China , in 2009. Currently, he is a researcher at School of Mechanical Engineering, Zhejiang University, China . His research interests include intelligent digital hydraulics and biological manufacturing based on fluid extrusion.

Xue Zhou received his B.S. degree in mechanical design and manufacturing from Xiamen University, China , in 2017. He is currently a PhD candidate at State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, China . His research mainly focuses on in vivo 3D printing and skin wound detection and repair.

Yichen Luo received his B.S. degree from Southeast University, China , in 2015. He is currently a PhD candidate at State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, China . His research mainly interests include 3D bioprinting and biological manufacturing.

Hao Zhang received his master degree from Zhejiang University, China , in 2019. Her research interests include deep learning and skin disease classification.

Huayong Yang received his PhD degree from the University of Bath, United Kingdom , in 1988. He is the dean of School of Mechanical Engineering, Zhejiang University, China . He was elected as an academician of the Chinese Academy of Engineering in 2013. His research mainly focuses on energy saving of fluid power and electromechanical systems and 3D printing of biological organs.

Jien Ma received her PhD degree from Zhejiang University, China , in 2009. She is currently a professor at College of Electrical Engineering, Zhejiang University, China . Her research interests include electrician theory and new energy technology.

Liang Ma received his PhD degree from University of Washington, USA , 2012. He is currently as an associate professor at School of Mechanical Engineering, Zhejiang University, China. His research interests include organ bioprinting and Microfluidic chip.

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Zhang, B., Zhou, X., Luo, Y. et al. Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning. Chin. J. Mech. Eng. 34 , 112 (2021). https://doi.org/10.1186/s10033-021-00629-5

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DOI : https://doi.org/10.1186/s10033-021-00629-5

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• Skin disease
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• Disease classification

10 Thesis Topics in Dermatology: How to Choose, How and Where to Research

Dermatology is a field of science that deals with the study of skin, nails, hair and its treatment in case of various complications. Selecting thesis topics in dermatology should be based on the sphere of interest and acquired knowledge obtained during the course. While choosing a particular topic, the researcher should pay attention to the innovative technological therapies that are elaborated to struggle with skin conditions. Thesis topics in dermatology should focus on the analysis of various surgeries, influence of a person’s lifestyle on the skin, and the relation of the skin condition to other diseases.

In this article, you are offered the list of 10 topics acceptable for dermatology theses – one of the research questions is answered for visual reference. Be sure that these dermatology topics are chosen according to the proper procedure that is also explained in this article. Besides, you’ll find 5 simple and effective ways to do research on dermatology topics. So read it attentively paying attention to all the details.

Table of Contents

10 Dermatology Topics: Be Open to New Thesis Ideas to Research

Don’t know what to research in Dermatology? Indeed, there are many possible dermatological issues that require a lot of attention on the part of researchers. But it is true that there may be difficulties in choosing a good dermatology topic, especially if you need to write a thesis that amounts to 50% of the overall grade. Among the key problems most students face while selecting a research topic, it is possible to highlight the following ones:

• There is no relevant information because the research topic has been only under close investigation;
• There is too much information because the topic is debatable and every researcher has his/her own point of view.

These 2 issues are taken into consideration while compiling the following list of 10 dermatology topics. It means that enough information is available to you to write a well-researched thesis on Dermatology. Below you’ll find the reliable sources of information.

• The Epidemiological Investigation of Uncommon Skin Disorders:
• Top 5 Risk Factors of Melanoma and Nonmelanoma Skin Cancers;
• The Genetic Test for Uncommon Skin Conditions;
• The Interaction Between Genetic and Environmental Factors for Skin Disorders;
• Genetic Changes within the FLG Gene and Negative Environmental Challenges for a Proper Skin Barrier;
• The Neonatal Skin Care Preventing the Development of AD;
• The Identification of Potentially Novel Skin Disorders within Technological Environment;
• The Interaction of Pharmaceutical and Cosmetic Agents for the Improvement of the Skin Barrier;
• Skin Manifestations of Autoimmune Disorders or Side Effects of Medication;
• The Effectiveness of Cosmetic Products in Treating Atopic Dermatitis.

10 Information Sources to Research a Dermatology Thesis Topic

It is vital to have reliable sources of information at hand before you start writing a thesis. Don’t skip this stage and start examining the following sources to write your own thesis:

• American Journal of Clinical Dermatology is a journal presenting the evidence-based articles and clinically focussed studies covering all aspects of dermatology.
• Annals of Dermatology is an official peer-reviewed publication of the latest research outcomes and recent trends in dermatology.
• Dermatology Case Reports Journal is a peer-reviewed journal that includes a wide range of topics in this field including Cosmetic Dermatology, Dermatology, Cosmetic Surgery, skin disorders, Dermatological Oncology, Dermatopathology, cutaneous lymphoma.
• Clinical, Cosmetic and Investigational Dermatology is a peer-reviewed journal covering the latest clinical and experimental research in all aspects of skin disease and its cosmetic interventions.
• Clinical Dermatology and Dermatitis is a peer-reviewed medical journal sharing the useful knowledge of clinicians, medical practitioners.
• Journal of the American Academy of Dermatology is a peer-reviewed journal containing official and scientific publications and aiming to satisfy the educational needs of the dermatology community.
• Journal of Investigative Dermatology is a peer-reviewed journal that is related to all aspects of cutaneous biology and skin diseases.
• JAMA Dermatology is is a monthly peer-reviewed journal by the American Medical Association that covers the diagnosis and treatment of all possible dermatological issues.
• The Skin Cancer Foundation is an international organization providing the public and the medical community with information about skin cancer. For example, you can examine some skin cancer facts and statistics to support your own research or essay.
• The Society for Melanoma Research is an organization formed by scientific and medical investigators to alleviate the suffering of people with melanoma.

3 Points to Choose the ‘Best’ Topic for a Thesis

Good research depends on many factors, and a well-chosen topic is that you should start with. You can know how to write and edit a thesis properly, but the final quality of the research process will depend on what topic is chosen. Make sure that the following points are applied to your thesis topic:

• Originality. A degree of originality is a key requirement for academic writing. Everyone hears about plagiarism issues at colleges or universities. In the case when you take into consideration the same topic that has been already explored, nobody will punish for that. However, you should keep in mind that it won’t be highly appreciated as well. Try to shed light on the issue from another perspective if you’ve already chosen an investigated matter. Otherwise, you risk not standing out in the academic field. Hopefully, you won’t pursue this path.
• Research interests. Always when you are short of ideas to cover, rely on the research interests – think of what could be interesting for people both in and outside the field of study, and get them excited about your research. In other words, your thesis should lead to answers for big important questions that are in mind of people.
• Manageability. Remember that developing any research idea means investing enough time and energy. However, there are some topics that are easy to consider, but much harder to write on. Think of the simplest way you will do your research, and how you would go about it. As a result, you should press ahead with the simple action plan first. Only then, you can make a final choice.

Although all these points play a great role in choosing a well-run topic for a thesis, you should stay within the proper context of the field of study to answer a research question to the fullest extent – an average idea that is well-executed is much better than a brilliant idea that is executed badly. Remember it and look at the example of writing on one of the dermatology topics.

The Impact of Hormones on the Skin

First and foremost, disbalance of hormones affects human skin that is caused by the number of problems such as consumption of non-organic food, inappropriate diet, and sugar balance, lack of sleep and exercise, and stress. Hormones are deeply integrated into chemical signals created in organs including adrenal glands, ovaries, and thyroid glands that influence other tissues. Estrogen, testosterone, and thyroid are the most important hormones that need to be regulated to have healthy skin and keep the body organism in balance.

Estrogen is primarily considered to be the female hormone that controls the reproductive system and fertility/libido levels. The decline of estrogens leads to the dehydration and poor skin as well as a small amount of blood flow to the skin. The skin becomes thin and sallow losing the accurate lines and healthy look. As a result, the wrinkles appear; the skin around the lips and eyes sags and loses its vibrancy.

To keep estrogens in balance, the person should consume natural foods adding flax seeds and soy to the diet that fasten estrogen metabolism. It helps to prevent the excess level of the hormone and protect the organism from such dangerous disease as breast cancer. Furthermore, herbs involving hops, maca, and black cohosh can also be used to increase estrogen levels in women. Bio-identical hormone therapy under the control of the well-trained professional can also be beneficial to regulate estrogen level.

Testosterone is a principally male hormone that is responsible for muscle and fat gain as well as stimulation of libido. This hormone helps to produce the sebum that is essential to keep the skin moist and nurturing. During the period of puberty and menopause, the levels of testosterone are on the rise that makes the skin too oily. That is why, in the teenage, individuals suffer from acne that may continue in the adult age if it is not treated. To manage hormone, people are recommended to avoid consuming dairy products and eat foods rich in zinc and omega.

The thyroid is another hormone which imbalance can cause dry skin or its thickening with reduction of sweat. On the contrary, the abundance of thyroid results in the smooth, flushed, and sweaty skin. The thyroid imbalance is exacerbated when the patient also faces difficulties with digestion and proper metabolism as well as fatigue. To improve the condition of the skin, one needs to consume fatty acids involving omega-3 that are present in walnuts, salmon, algae, and eggs. The poor diet lacking these fats leads to acne and makes the skin dry.

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The thesis topics are compiled by the respective SIG. These are only ideas and not well-formed research questions and are required to be modified by the researchers as per the feasibility and relevance. Researchers are advised to select appropriate methodology too.

SIG Aesthetics

• MNRF with PRP vs MNRF in striae – A comparative, clinico- histopathological study.
• The efficacy of PRF or Biofiller in Acne scars – A clinicohistopathological study with grading of scars.
• Dermal threads vs 5% minoxidil in androgenetic alopecia
• Botulinum toxin vs Microneedle Radiofrequency in axillary hyperhidrosis – A comparative assessment
• PRP vs tranexamic acid mesotherapy – A split face comparative study
• Scar revision outcome analysis with and without botulinum toxin
• DLQI of patients attending aesthetics clinic

SIG Dermatology Clinical Research

• Randomized controlled trial comparing the efficacy and safety of intralesional vitamin D versus cryotherapy in the treatment of verruca vulgaris
• A randomized controlled trial comparing the efficacy and safety of intradermal MMR versus 10% KOH in the treatment of molluscum contagiosum in children
• A randomized controlled trial comparing the efficacy and safety of topical timolol versus oral propranolol in the treatment of infantile hemangioma
• A randomized controlled trial comparing the efficacy and safety of oral cochicine versus oral isotretinoin in the treatment of lichen planus pigmentosus
• A randomized controlled trial comparing the efficacy and safety of oral Cyclosporine versus oral corticosteroids in the treatment of atopic eczema
• A randomized controlled trial comparing the efficacy and safety of daily oral corticosteroids versus mini-pulse in the treatment of rapidly progressive non-segmental vitiligo
• Risk factors for STI in CSWs/ MSM/ STI clinic attendees
• Prevalence of acquired ichthyosis in diabetes and its relationship with systemic complications
• Costing of care in pemphigus
• Comparison of quality of life / expenses in various treatments in psoriasis.
• Prevalence and outcome of telogen effluvium in primi gravida
• Quality of life outcomes in acne vulgaris
• Assessment of insulin resistance and metabolic panel in psoriasis, acne, acne inversa, AGA, vitiligo.
• RCT comparing the efficacy and safety of excimer vs targeted broad band UV B in the treatment of focal, non-segmental vitiligo
• Dermoscopy in topical steroid damaged face
• Validating a severity score in TSDF
• RCT – microneedling vs MNRF for acne scars
• RCT – microneedling vs MNRF for striae
• RCT single wavelength vs combination lasers in hair removal
• Risk factors for development of infantile hemangioma
• Assessment of neuropsychatric manifestations in atopic eczema
• Quality of life in patients with alopecia areata
• ACE receptors in hair roots- Could that explain severe hair loss post Covid?
• Sexual problems in patients suffering from genital psoriasis.
• Topical tofacitinib for acral vitiligo
• Compliance of treatment in acne patients - clinical factors
• Efficacy and safety of DPCP in AA
• Dermoscopy in follicular disorders
• Oral minoxidil for AGA

SIG Dermatopathology

• Dermoscopic and histopathologic correlation in lichen planus
• Clinical, histopathological and immunohistochemical study of CD4 and CD8 T lymphocyte subsets in lichen planus
• Role of plasmacytoid dendritic cells in differentiating conditions with interface dermatitis
• Histopathological features of lichen sclerosus et atrophicus
• Clinicopathological correlation in connective tissue diseases and correlation with ANA titer
• Study of clinical and histological features of pityriasis rosea, pityriasis lichenoides, guttate psoriasis: a comparative study
• Clinical, histopathologic and immunofluorescence study in cutaneous amyloidosis
• Clinico-dermoscopic-histopathological correlation of cicatricial alopecias
• Clinico-immunopathological correlation in vasculitis
• Study of T lymphocytes (CD4, CD8, regulatory cells), B lymphocytes, natural killer cells, FOXP3, TGF-β1 AND IL-10 mRNA levels in blood using real time PCR and expression of CD4, CD8, granzyme B, TGF- β1 and IL-10 in skin biopsy using IHC in vasculitis and their comparison post treatment
• Invisible dermatoses: a histological spectrum (clinically invisible)/ True invisible dermatoses (clinical and histological), can CPC help?
• Neutrophils in stratum corneum on an acanthotic epidermis: a spectrum
• Dermoscopic-histologic correlation in vitiligo activity
• Histopathological spectrum of cutaneous reactions to novel targeted chemotherapeutic agents
• Clinico-histopathologic study of erythrodermas
• Clinico-histopathological study of porokeratosis
• Clinico-histopathological spectrum of panniculitis
• Clinico-histopathological spectrum of allergic contact dermatitis
• Clinico-histopathological correlation in prurigo nodularis
• Clinico-histopathologic study of reactions to tattoos
• Eosinophilic dermatoses: A clinical and histopathological spectrum
• Correlation of BI in slit skin smears (SSS) versus histopathology
• Sensitivity of earlobe SSS in patients with no clinical ear lobe thickening
• Sensitivity of SSS vis a vis type of leprosy
• Clinicopathological and radiological correlation in Hansen’s disease
• Dermoscopic and histopathological correlation in various poles of leprosy: a prospective study
• Macrophage differentiation (M1/M2) in different types of leprosy and correlation with treatment outcome
• Role of B cells and plasma cells in leprosy
• Role of regulatory T cells in leprosy and correlation with treatment outcome
• Study of different types of granulomas in common granulomatous disorders
• Clinico-histopathologic study of rash in secondary syphilis
• Clinico-pathological correlation of subcutaneous/ deep fungal infections
• Necrobiotic granulomas: A clinical and histopathological spectrum
• IHC vs. DIF in immunobullous disorders
• Clinico-histopathologic study of epidermolysis bullosa
• Clinico-histopathologic and immunofluorescence study in dermatitis herpetiformis
• Role of DIF in sub-epidermal bullous diseases
• Dermoscopic-histologic correlation in skin tumors
• Immunohistochemistry as an aid in the diagnosis of adnexal tumors
• Comparative immune-histochemical studies for mycosis fungoides and parapsoriasis
• Benign vs. malignant cutaneous lymphoid infiltrates: role of histopathology and immune-histochemical studies

SIG Dermoscopy

• Dermoscopic in monitoring treatment of warts.
• Dermoscopic and histological comparison of palmoplantar eczema and palmoplantar psoriasis.
• Dermoscopy in noninfective granulomatous disorders.
• Dermoscopic prognostic factors of alopecia areata in relation to intralesional triamcinolone actonide monotherapy.
• A cross-sectional study of clinicodermoscopic features of various causes of pigmentation of the face of middle-aged individuals (acanthosis/ postinflammatory hyperpigmentation/amyloidosis/melasma/ maturational hyperpigmentation).
• Comparative dermoscopic study of alopecia areata and trichotillomania.
• Dermoscopic features of cutaneous T cell lymphoma - a cross-sectional study.
• Clinico-dermoscopic and histopathological correlation in nail tumors.
• Therapeutic monitoring of scabies treatment by dermoscopy.
• Clinical and dermoscopic features of palmoplantar keratodermas.
• A clinical and dermoscopic study of inflammatory follicular disorders.
• Onychoscopy to evaluate treatment response in onychomycosis.
• Dermoscopy to evaluate treatment response in rosacea/ demodicosis.
• Clinico-dermoscopic study of topical steroid damaged facies (TSDF).
• Clinicodermoscopic assessment of facial aging - a comparison between males and females.
• Role of dermoscopy in assessing therapeutic response to tacrolimus or topical clobetasol in limited alopecia areata.
• Role of dermoscopy in differentiating guttate vitiligo, idiopathic guttate hypomelanosis and guttate lichen sclerosus.
• A longitudnal study to delineate specific signs of alopecia areata across the subtypes - Acute, chronic, patchy, total. (histological correlation if feasible)
• Dermatoscopic features of macular amyloidosis.

SIG Dermatosurgery

• Surgical intervention in keloids and relapse rates
• Dermaroller vs Fractional CO2 laser with/without PRP in acne scars (split face study)
• Nail biopsy success rate in diagnosis
• Radio frequency subcision vs subcision in acne scars
• Platelet rich fibrin efficacy in periorbital wrinkles
• Intralesional triamcinolone with or without dermaroller in alopecia areata
• Dermaroller with or without PRP in split scalp trials for AGA
• Comparative study of different vitiligo surgeries on bilateral lesions
• Comparison of PRP preparation with different techniques to assess platelet counts
• Potential scarification of scalp with multiple PRP sessions and its effect on results of hair transplant

SIG Female Genital Dermatoses

• Study on Psychological morbidity in vulvar pruritus
• Study of sexual dysfunction in cases of painful genital conditions
• Pediatric vulvar dermatoses- etiopathogenic and clinical study
• Clinical and etiological study of vulvovaginal itching
• Clinical and etiological study of inflammatory vulvar dermatoses
• Assessment of female sexual dysfunction and quality of life in females with chronic vulvar dermatoses
• Clinical and etiopathogenic study of vulvovaginal discharge
• Questionnaire based study on genital hygiene practices in women with and without chronic vulvar dermatoses
• Combined effect of MNRF and Fractional CO2 Laser in moderate to severe acne scars
• Effect of fractional CO2 in hypertrophic scar
• Fractional CO2 or erbium YAG laser in Tentative cuts
• Comparative study of effect of nail lacquer versus fractional CO2 laser in onychomycosis
• Q switched versus LP Nd YAG laser in onychomycosis
• MNRF in Androgenetic alopecia
• Q switched and YAG for the treatment of Xanthelesma palpebrum.
• Ultra pulse co2 laser versus Q switched Nd YAG for mole removal comparative study
• Efficacy of fractional co2 laser (vaginal probe ) for vaginal tightening
• Combination of Fractional CO2 and PRP in stable non-segmental vitiligo.
• Combined fractional and q switched Nd Yag lasers for tattoo removal
• Combination of Q switched Nd Yag and long pulse Nd Yag for Beckers nevus
• Fractional Mnrf Vs co2 laser split face study
• Q switched nd yag laser vs co2 and q switched combined for tattoos
• Co2 laser efficacy in keloids
• CO2 laser plus PRP efficacy in striae
• Laser hair reduction in skin of colour.
• Treatment of Spider Veins Using 810 nm Diode Laser
• Difficult to treat scars management with combination therapy.
• A comparative study of q switched ndYag + fractional CO2 versus Q switched ndyag alone for tattoo removal
• Comparative study of triple wavelength hair removal laser low fluence multiple pass to high fluence single pass
• Treatment of hidradenitis suppurative with lasers
• Laser treatment of Acne keloidalis nuchae
• Treatment of hidradenitis suppurativa with lasers
• Laser treatment for periorbital melanosis
• Laser treatment for Lip pigmentation
• Comparative study of Fractional CO2 laser v/s LP/QSw Nd:YAG laser for for the treatment of Onycomycosis.
• Comparative study of Fractional CO2 v/s MNRF for striae.
• Q SW Nd:YAG v/s combination of Fractional CO2 + Q Sw NdYAG for Nevus if Ota
• Comparative study of pin point CO2 laser v/s intralesional RF for papular acne scars.
• Efficiency of Gold Toning + topical Clindamycin for acne v/s only Gold toning.
• Fractional Nd:YAG for melasma
• Q SW Nd:YAG v/s arginine peel for periorbital hypermelanosis

SIG Leprosy

• Assessing effectiveness of alternative drug regimes in Leprosy
• High frequency ultrasound to study the changes in nerve pre and post treatment
• Comparative histopathology of skin and nerve in leprosy patients
• Study of oxidative stress in lepra reaction
• Observational study on ocular changes in leprosy in the post elimination era
• Assessing effectiveness of prophylactic prednisolone vs therapeutic prednisolone for leprosy neuropathy
• Use of Immunofluorescence microscopy (Rhodamine-O) staining Vs Slit skin smear for Acid fast bacilli
• Efficacy of alternative antimicrobials in the treatment of leprosy
• Assessing efficacy of chemoprophylaxis/immunoprophylaxis in leprosy
• Psychosocial burden in leprosy
• Social stigma and leprosy
• Dermatoscopic evaluation of cutaneous lesions of leprosy
• Nutritional assessment of patients with leprosy
• Clinical and histopathological evaluation of safety, efficacy, tolerability of current three drug MDT PB regimen vs the two-drug regime in paucibacillary Hansen's disease
• Plantar arch assessment in patients with plantar hypoesthesia and trophic ulcers in Leprosy
• An exploratory study of Microbiomes in plantar ulcers
• Study of Grade 2 disabilities in new leprosy patients
• Study of grade 2 disabilities in childhood leprosy
• Study of childhood leprosy in a Dermatology OPD over 1 year period
• Study of leprosy in elderly leprosy patients > 60 years of age
• Study of residual deformities and disabilities in Released From Treatment (RFT) patients
• A study of benefits of use of EMLA cream before performing slit skin smears in suspected leprosy patients
• Clinical and histological study of BB leprosy and its annular / ring shaped lesions
• Use of different regimens/ schedules of prednisolone in type 1 reactions in leprosy patients
• Study of use of thalidomide in type 2 reactions, its dosage, tolerability and adverse effects
• Slit skin smear examination in MB leprosy: inter-site variations
• Use of MiP vaccine as adjuvant in MB leprosy- clinico-histologial study
• Dermatoscopic study of clofazimine induced pigmentation
• Study of facial lesions in leprosy patients
• Study of use of SW filaments in sensory assessment in young v/s older leprosy patients
• Use of cosmetic camouflage in facial leprosy patches for improving Life quality index

SIG Neglected tropical diseases

• Study on type of cutaneous tuberculosis and its dermoscopic features
• Comparative study on oral ivermectin + topical ivermectin and oral ivermectin + topical permethrin in scabies and ivermectin alone
• Secondary bacterial infection in scabies patients and its determinants
• Incidence and clinical characteristics of childhood leprosy. Observational study in tertiary care hospital
• Histopathological patterns of cutaneous tuberculosis
• Histopathological study of Cutaneous leishmaniasis
• Dermatological manifestations in lymphatic filariasis and their management
• Clinical, microbiological (smears and culture) and histopathological characteristics of subcutaneous mycosis and mycetoma
• Positivity rate of LD bodies in cutaneous leishmaniasis tissue smear and factors modifying it
• To study the patterns of treatment response and multi-drug resistance in late / non-responders to first line anti-tubercular treatment in cutaneous tuberculosis
• Molecular identification and antifungal susceptibility testing in subcutaneous mycoses
• Comparison of tissue culture and polymerase chain reaction in the diagnosis of atypical mycobacterial infections
• Apremilast in type 2 Lepra reaction
• Azathioprine in type 2 lepra reaction
• Factors affecting Grade 2 disability in leprosy
• Comparison of IL-17 levels among leprosy patients with and without reaction

SIG Pediatric Dermatology

• Comparative study of efficacy of topical Ozenoxacin versus Mupirocin in Impetigo Contagiosa
• Efficacy and Safety of Apremilast in childhood Alopecia Areata
• Spectrum of Ocular changes in Pediatric Atopic Dermatitis : A Observational study
• Comparative study on PRP versus ILS in pediatric Alopecia Areata
• RCT on the safety and efficacy of Bilastine vs Levocetrizine in Chronic spontaneous Urticaria
• RCT on JAK inhibitor Tofacitinib vs conventional OMP in the management of Childhood Vitiligo
• Clinico epidemiological profile of Psychocutaneous Disorders in Adolescence
• Dermoscopic and Histopathological correlation in Pediatric Psoriasis
• Clinical, dermoscopic and epidemiological study of alopecia in pediatric age group.
• Spectrum of nutritional dermatoses in pediatric age group
• Spectrum of SCAR - severe cutaneous ADR in pediatric group
• Clinical and investigative study of Autoimmune Connective Tissue Disorders in pediatric age group
• PPD verses Vit D3 injections in warts in peadiatric pts, comparitive study

SIG Pigmentary diseases

• Suction blister graft versus excimer for acro facial lip vitiligo
• Dermascopy in ADMH
• A clinico-histopathologic and dermoscopic correlation of facial melanosis
• Patch test in facial melanosis
• Endocrinal profile of progressive vitiligo

SIG Pruritus

• To evaluate the burden of chronic pruritus in cancer patients in an oncology center
• Clinical and immune-pathological study of chronic pruritus in geriatric population
• Study of genital itch in patients attending dermatology and gynecology outpatient department in a tertiary care center
• Clinico epidemiological study of scalp pruritus
• Clinical and immune-pathological study of chronic prurigo
• Post herpetic itch - A clinico epidemiological study
• To evaluate association of hematological parameters with severity of itch in chronic pruritus of non dermatological origin
• Pruritus ani: A clinico-investigative study

SIG Psoriasis

• Lipid Accumulation Product Index as Visceral Obesity Indicator in Psoriasis
• Prevalence of Metabolic Syndrome in Psoriasis Patients and its Relation to Disease Duration
• Atherogenic index of plasma in psoriasis patients
• C-reactive protein and cardiovascular risk in patients with psoriasis.
• Dyslipidaemia & oxidative stress in patients of psoriasis
• Comprehensive lipid tetrad index as a marker for increased cardiovascular risk in psoriasis
• Methotrexate vs Apremilast in treatment of palmoplantar psoriasis
• Erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor and anti- cyclic citrullinated peptide antibodies in nail psoriasis
• Coexistence of onychomycosis in psoriatic nails
• Alcoholism in psoriasis
• Comorbidities in childhood psoriasis
• Renal dysfunction in chronic plaque psoriasis
• USG evaluation of enthesial thickness in psoriasis patients
• Mometasone Vs Mometasone + Tazarotene in management of limited lesions of plaque psoriasis
• Incidence of Co-morbidities in patients having moderate psoriasis (PASI>5)
• Comparative study of therapeutic response with oral systemic drugs in patients with moderate psoriasis ( metho, Acetretin, cyclosporine, alert,tofacitinib) 10-15 patients in each group
• Correlation between histopathologic and dermoscopic findings in various types of psoriasis
• Comparison of NBUVB VS NBUVB plus Apremilast in moderate to severe psoriasis- an observational study
• Evaluation of serum “THYMIC STROMAL LYMPHOPOIETIN (TSLP) in Psoriasis patients in comparison to controls- a case-control study
• Correlation of CXCL 10 and PEST (Psoriasis Epidemiology Screening Tool ) in patients of Psoriasis-A prospective study
• Study of health-related Quality of life in moderate to severe psoriasis pediatric age group
• Evaluating the efficacy and safety of Apremilast in refractory scalp psoriasis-A prospective study

SIG Recalcitrant Dermatophytoses

• Correlation of MIC levels, mutations with clinical response to Antifungals
• Role of immunity  in extensive,  atypical and aggressive  Dermatophytosis
• Intra familial cases
• Clinico-mycological study of the association of onychomycosis with chronic dermatophytosis versus naive cases of dermatophytosis
• Clinical, dermoscopic and mycological study of tinea pseudo imbricata
• Clinical epidemiological and mycological study of dermatophytosis in children
• Clinical epidemiological and mycological study of dermatophytosis in pregnancy
• Adverse drug reactions to oral antifungal drugs
• Prevalence of atopy in chronic dermatophytosis
• The sensitivity of oral antifungals in immunocompetent versus immunosuppressed individuals
• Treatment response when oral terbinafine is combined with topical terbinafine versus oral terbinafine with azole topical
• Resistance studies vis a vis species of dermatophyte

SIG Trichology & hair transplant

• Comparison of PRP VS IPRF -split scalp prospective study
• Finasteride vs Dutasteride in patterned hair loss
• Tofacitinib in alopecia areata, comparative analysis of 5mg vs 10mg vs 20mg
• Comparative study of Topical minoxidil with finasteride vs dutasteride lotion
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An Explainable AI-Based Skin Disease Detection

• Conference paper
• First Online: 08 November 2022
• Cite this conference paper

• Gayatri Shrinivas Ballari   ORCID: orcid.org/0000-0003-3929-3819 12 ,
• Shantala Giraddi   ORCID: orcid.org/0000-0001-8127-6444 12 ,
• Satyadhyan Chickerur   ORCID: orcid.org/0000-0002-5629-4883 12 &
• Suvarna Kanakareddi 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 520))

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L ack of understanding and ignorance has led to, skin illness being one of the most frequent and difficult diseases to diagnose. Dermatologists are frequently sought for in many underdeveloped countries for skin problems and prevention. People are unsure about the dermatologist’s medical prescriptions, and there is no reason for this method. Because skin plays a large role in defending the form against fungal and dangerous bacterial infections, it is critical to recognise disease early on and not ignore it. As a result, detecting disease and making a diagnosis early is critical. As a result, in order to provide a feasible and efficient system, particularly in the light of the growth of image processing-based disease analysis, more reminders may be provided. People can submit input through camera techniques, and image processing, and machine learning techniques are integrated; the respective skin problem is recognised, and diagnosis is sometimes indicated. Machine-driven identification of skin illness using deep learning/machine learning techniques is the main goal of this project. In this study, ResNet18 is trained, the model is valued, and the Grad-CAM model explainability technique is used to help dermatologists better understand the model’s predictions. The ResNet18 is the end product of deep learning. The accuracy of the classification model is 96%. This paper presents a survey of the deep learning clarifying literature on skin disease diagnosis using structural photos. The difference between what dermatologists consider explainable and what existing tactics provide is discussed, as well as future proposals for closing the gap.

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Madgi M, Giraddi S, Bharamagoudar G, Madhur MS (2021) Brain tumor classification and segmentation using deep learning. In: Smart computing techniques and applications. Springer, Singapore, pp 201–208

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School of Computer Science Engineering, KLE Technological University, Hubballi, 580031, India

Gayatri Shrinivas Ballari, Shantala Giraddi, Satyadhyan Chickerur & Suvarna Kanakareddi

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ITM University, Gwalior, Madhya Pradesh, India

Shyam Akashe

Global Knowledge Research Foundation, Ahmedabad, India

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Ballari, G.S., Giraddi, S., Chickerur, S., Kanakareddi, S. (2023). An Explainable AI-Based Skin Disease Detection. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_30

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DOI : https://doi.org/10.1007/978-981-19-5331-6_30

Published : 08 November 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5330-9

Online ISBN : 978-981-19-5331-6

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• Indian Dermatol Online J
• v.5(1); Jan-Mar 2014

Skin diseases and conditions among students of a medical college in southern India

Nitin joseph.

Department of Community Medicine, Kasturba Medical College, Manipal University, Mangalore, India

Ganesh S Kumar

1 Department of Community Medicine, JIPMER, Puducherry, India

Maria Nelliyanil

2 Department of Community Medicine, A. J. Institute of Medical Sciences, Mangalore, India

Introduction:

Skin diseases are a common problem among young adults. There is paucity of data about it among medical students. This study aimed to find out the pattern of skin disorders and to describe their association with various socio-demographic factors among medical students.

Materials and Methods:

This cross-sectional study was conducted in June 2011 in a medical college in Mangalore, Karnataka. Two-hundred and seventy eight medical students were chosen from the 4 th , 6 th and 8 th semester through convenient sampling method. Data on hair and skin morbidities suffered over past 1 year and its associated factors were collected using a self-administered questionnaire.

Most of the participants 171 (61.5%) were of the age group 20-21 years and majority were females 148 (53.2%). The most common hair/skin morbidities suffered in the past one year were acne 185 (66.6%), hair loss 165 (59.3%), and sun tan 147 (52.9%). Fungal infection ( P = 0.051) and severe type of acne ( P = 0.041) were seen significantly more among males while hair morbidities like hair loss ( P = 0.003), split ends of hairs ( P < 0.0001) and dandruff ( P =0.006) were seen significantly more among female students. Patterned baldness ( P = 0.018) and sun tan ( P < 0.0001) were significantly more among non-Mangalorean students than native Mangaloreans. Presence of dandruff was significantly associated with hair loss ( P = 0.039) and usage of sunscreen was found to protect from developing sun tans ( P = 0.049).

Conclusion:

Skin disorders, particularly the cosmetic problems are very common among medical students. Gender and place of origin were found to significantly influence the development of certain morbidities.

INTRODUCTION

Skin diseases are a major health problem affecting a high proportion of the population in India.[ 1 ] Skin diseases can place a heavy emotional and psychological burden on patients that may be far worse than the physical impact.[ 2 ] Increased consciousness especially among the youth of their body and beauty further aggravates their anxiety.[ 3 ]

Many factors determine the pattern and prevalence of cutaneous diseases among the youth such as gender, race, personal hygiene, quality of skin care, environmental milieu and diet.[ 4 ] In some instances, patients appear to produce their skin lesions as an outlet for nervous tensions arising from interpersonal conflicts and/or unresolved emotional problems.[ 5 ]

Even though dermatology is characterized by an enormous range of disease/reaction patterns, prevalence surveys suggest that the bulk of skin diseases belong to fewer than ten categories.[ 6 ] Such observations are useful in developing educational and preventive health programs for the benefit of university students. Their proper management at earlier stages with education of students is important to prevent disfiguring complications and psychological sequelae later in life.[ 3 ]

However, very few studies have been carried out in India to find out the problem of skin diseases and that especially among the medical students. The reason for this negligence could be the low mortality rate of the majority of skin diseases in comparison with other diseases. This has also resulted in international health policy makers and local decision makers to make dermatological morbidities a low priority.[ 7 ] Another concern is that the benefits of public health interventions in reducing the prevalence, morbidity and mortality of skin diseases may be underestimated.[ 8 ] Thus there is a need for more studies with respect to dermatological morbidities in a developing country like India. With this background, this study was carried out to find out the pattern and severity of skin disorders and to describe their association with various socio-demographic factors among medical students of a private medical college in Mangalore city of south India.

MATERIALS AND METHODS

This cross-sectional study was done in June 2011. The ethical approval for conducting this study was obtained from institutional ethics clearance committee. A sample size of 278 was determined using a confidence level of 95%, with 15% degree of precision of the expected proportion and an estimated minimum prevalence of 40%. These students were chosen from the 4 th , 6 th and 8 th semester through convenient sampling method so that the sample will have a balanced representation of 2 nd , 3 rd and final phase medical students of the institution.

The students were briefed about the objective of the study and written informed consent was taken for participation. A pre-tested self-administered semi-structured questionnaire was used for data collection. The face validity of this questionnaire was done by an expert in dermatology who reviewed the contents of the questionnaire. The questionnaire was subjected to a pilot trial on 10 students before it was distributed in its final form. Reliability of the questionnaire was assessed using Cronbach's Alpha the value of which was 0.82 indicating good internal consistency. Questions on the presence of any skin morbidities suffered by the student participants in the past 1 year were asked.

Additionally questions like frequency of face wash in a day, usage of facial cleansing products, frequency of head and body bath in a week, frequency of usage of hair shampoo in a week, usage of sunscreen lotions, moisturizers or cosmetics, frequency of changing into new clothes, habit of sharing linen with friends and promptness in seeking dermatologist consultation for skin ailments were asked to assess the quality of skin care.

Life style habits were assessed based on amount of water consumed in a day, frequency of eating fatty or oily food stuffs in a week, frequency of consumption of fruits and vegetables in a week, smoking habits and recreation habits like swimming.

Each response for the question meant to assess quality of skin care and life style habits were given scores from 0 to 2. Scores from 0 to 11 for questions deciding quality of skin care meant poor, 12-22 meant good level of skin care. Similarly scores from 0 to 5 for questions deciding life style meant poor and 6-10 meant good level of lifestyle habits.

The data entry and analysis were done using Statistical Package for Social Sciences software package (SPSS Inc., Chicago, IL) version 16. Chi-square test was used to find out the association of socio-demographic variables with the presence of skin morbidities, quality of skin care and life style habits P < 0.05 was taken as statistically significant association.

Mean age of participants was 20.35 ± 1.23 years [ Table 1 ].

Age, gender and place distribution of students

Of the 278 students, 69 (24.8%) had fair skin, 120 (43.2%) had wheatish skin, 74 (26.6%) had brown skin and 15 (5.4%) had dark skin. The one- year-period prevalence of various skin morbidities showed acne to be the commonest skin morbidity in 185 (66.5%) cases followed by sun tan in 147 (52.9%) cases. Among the hair morbidities commonest was hair loss seen in 165 (59.3%) cases followed by dandruff seen in 129 (46.4%) cases [ Table 2 ].

Association between various hair/skin morbidities among students with gender

Fungal infection was seen significantly among a greater proportion of males while among females the significant morbidities were hair loss, split end of hairs and dandruff [ Table 2 ].

Patterned baldness and sun tan were seen significantly more among greater proportion of non-Mangaloreans than native Mangaloreans [ Table 3 ].

Association between various hair/skin morbidities among students with place of origin ( n =278)

White/black heads were seen significantly more among females while papular and pustular types of acne were seen significantly more among a greater proportion of males. The proportion of cases with pustular type of acne was 30 (10.8%) [ Table 4 ].

Association between gender with type and duration of acne

Of the 278 students with morbidities, 236 (84.9%) had good quality skin care and the rest had poor quality skin care. 108 (83.1%) males and 128 (86.5%) females reported good quality skin care ( P = 0.428). Among the participants with good quality skin care, 161 (68.2%) reported presence of morbidities whereas among participants with poor quality skin care, 24 (57.1%) reported presence of morbidities ( P = 0.161).

Of the 278 students with morbidities, 236 (84.9%) had good life style habits and the rest had poor life style habits. One hundred and seven (82.3%) males and 129 (87.2%) females reported good life style habits ( P = 0.259). Among the participants with good life-style habits, 162 (68.6%) reported presence of morbidities, whereas among participants with poor life-style habits, 23 (54.8%) reported presence of morbidities ( P = 0.079). Out of 129 cases with history of dandruff, hair loss was present in 85 (65.9%) cases ( P = 0.039).

Usage of sunscreen in hot sun was associated with significant reduction in proportion of cases with sun tan among the participants [ Table 5 ].

Association between presence of sun tan with usage of sun screen among students

It has been found that one- fourth of us (or more) suffer from at least one skin disease, a situation that constitutes a significant global burden of disease.[ 9 ] Economic burden of skin diseases is enormous and added to this easy visibility of dermatological illness has led to deterioration in the quality of life resulting in social handicap.[ 10 , 11 ]

In certain parts of the world, it was observed that the mortality rate and disability-adjusted life years due to skin diseases were at par with certain communicable and non-communicable diseases.[ 7 ] In a regression model, skin diseases as well as rheumatism was more strongly associated with feeling depressed than asthma, diabetes and angina pectoris.[ 12 ] Considering their significant impact on the individual, the family, the social life of patients and their heavy economical burden, the public health importance of these diseases is underappreciated.[ 8 ] This study too has shown that various types of skin morbidities are common among medical students. It has been reported that younger adults suffer more social problems as a result of skin problems than older adults.[ 12 ] Thus control of skin morbidities will definitely lead to improvement in the quality of life of young adults. In this study the most common morbidity reported was acne followed by hair loss which was also supported by other studies.[ 3 , 13 ]

Acne has been incriminated with sweating and hot weather, which is very compatible with the hot and humid climatic conditions prevailing in Mangalore.[ 14 ] The proportion of severe acne cases in this study was 10.8% which was more than the observation of 5.4% made in the Sindh based study.[ 3 ] Studies carried out in other countries have found that acne is a disfiguring disease and it should not be looked at as trivial,[ 15 ] as it may seriously affect the patient's life.[ 16 ] Screening adolescents for conditions like acne may be of great importance because it affects their image in the society and because of the wide armamentarium of therapy which is available.[ 17 ]

Hair loss was the next most common problem, which is very much global in nature. The true magnitude of problem is difficult to establish from this study as the data on the hair density and thickness in our subjects was lacking. There was significant association of dandruff as a risk factor for hair loss in this study which was similar to the findings of other studies.[ 3 , 18 ] However, in the absence of any apparent systemic or local cause for generalized hair loss, it can be assumed that constitutional factors or micro-deficiency of iron, vitamins and proteins may be the cause of hair loss in these subjects.[ 19 , 20 ]

Hair loss culminating in baldness is another sensitive issue among adolescents as they are invariably sensitive regarding their external features and thus may be easily withdrawn psychologically and avoid social activities due to androgenetic alopecia and this tends to affect girls more than boys.[ 21 ] In this study almost a quarter of students had baldness with greater proportion observed among males.

Increased tanning of skin was the third most common morbidity. This was understandable as 68% of the participants had fair or wheatish skin. This skin type is prone to tanning on sun exposure. Being less aware of the tanning effect of sun light and not using personal protective measures while outdoors must have promoted tanning and darkening in these subjects.[ 22 ]

Fungal infections were reported by more than a third of our participants in the past 1 year. Previous studies have reported that periods of high humidity (50-80%) and elevated temperatures reaching up to 35°C are ideal for fungal infections.[ 17 ] This probably could explain the reason behind a number of cases with fungal infections among students in Mangalore.

In a study carried out among university students in Sindh, Pakistan acne was seen in 59.5%, hair loss in 59%, pigmentary disorders in 36.3%, dandruff in 26.1% and fungal infection in 4.9% of the cases. All these observations made were lower than our findings. The study also found pruritis among 2.3% of the cases and eczema among 2.1% of the cases.[ 3 ] In another study carried out among 1279 university medical students by Roodsari et al ., 91.7% students had skin morbidities. Here acne was seen in 56%, hair loss (evaluated only in females) in 14%, dandruff in 11%, hand eczema in 10%, seborrheic dermatitis in 9% and pityriasis versicolor in 8% cases.[ 13 ] But for acne which is easily identifiable, the other skin morbidities were higher in this study than ours probably because disease identification in the former study was done by dermatologists unlike our study where it was self-reported by students. An Icelandic study found that the prevalence of urticaria was significantly higher among the medical students and was seen in 41% of students.[ 23 ] These variations in morbidities among students of same age group in different parts of the world could be due to racial, genetic and environmental variations.

In this study acne was found to be slightly more and hair problems was seen significantly more among females, which was similar to the findings of a study done among university students in Lebanon where both acne and hair problems were significantly more among females.[ 17 ]

Although there was no significant difference between the proportion of males and females with acne in the present study, the type of acne differed significantly between the two groups. White/black heads were seen significantly more among females while papule and pustule were seen significantly more males. This was similar to the observation made in another study carried out in New Zealand where severe type of acne was seen more among males.[ 24 ] Severity of this condition among males could be because of hormonal factors.[ 25 ]

Fungal infection seen significantly more among males in this study could be due to their lesser quality of skin care and life style habits in comparison to females. Other cutaneous disorders like pyoderma, folliculitis, scabies and pediculosis were not seen in this study. The reason for absence of these bacterial and parasitic infections could probably be that very few participants in this study had poor quality of skin care or hygiene. No cases of eczema, hyper pigmentary lesions like melasma, hypopigmentary lesions like vitiligo, nail disorders or skin cancers were reported by any of the participants.

Sun tans were seen significantly more among a greater proportion of non-Mangaloreans than native Mangaloreans. This could probably be explained by the non-adjustment to the hot and humid conditions of Mangalore among the outstation students. It was also observed that the users of sunscreen had significantly less cases of sun tans compared to non-users, signifying the importance of spreading awareness about the usage of such protective methods.

Limitations

The present study may not be generalized to other population groups because of different factors associated with different skin morbidities. It may not reveal the true burden of skin disorders among young adults as much as a population-based study. Also as these morbidities were self-reported there may be a possibility of recall bias. In this study, quality of skin care was assessed based on frequency of activities like face wash or body bath or based on the frequency of usage of hair shampoo or sunscreen lotions or moisturizers or cosmetics. Since the quality of these activities or products as well as its proper application on the body was not enquired, it could be a limitation in estimating the true quality of skin care.

Moreover it was difficult to differentiate between the physiological and pathological conditions in hair loss. The most important drawback of this study was that few skin morbidities might have been diagnosed by medical students themselves without actually consulting a dermatologist leading to inaccurate self-reported diagnosis. Hence more of such studies from a broader socioeconomic spectrum are required, which need to be suitably supported with dermatological examination of study subjects.

From the findings of one- year- period prevalence of various skin disorders we conclude that skin morbidities are very common among medical students, particularly cosmetic problems like acne, hair loss and skin tan. Severe types of acne and fungal infections were significantly more among males whereas hair morbidities were significantly more among females. Patterned baldness and sun tans were seen significantly more among non-Mangalorean students than native Mangaloreans. This emphasizes the need to popularize the importance of personal protective measures like usage of sun screens among students. Establishment of registries for specific skin diseases, particularly for those with a high disease burden will also help in good case accountability stressing importance to dermatological public health.

ACKNOWLEDGMENTS

The authors of this study would like to thank M.B.B.S students, Ms. Monica N, Mr. Ishan Parashar, Ms. Hemashri, Ms. Supraja Subramanian, Ms. Liya Susan Peter, Ms. Anupriya Dalmiya and Ms. Akanksha Bansal of K.M.C Mangalore for their help in data collection. We also thank Dr. Mohan Kudur, Associate Professor, Department of Dermatology, Venereology and Leprology, Srinivas Institute of Medical sciences and Research Centre, Mangalore for his help and support.

Source of Support: Nil

Conflict of Interest: None declared

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Eke, Gozde. "Biopolymer Based Micro/nanoparticles As Drug Carriers For The Treatment Of Skin Diseases." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613878/index.pdf.

Zhang, Ziwei [Verfasser]. "Development of Novel Semisolids for the Treatment of Chronic Skin Diseases / Ziwei Zhang." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1227973497/34.

Summers, Jennifer F. "The effectiveness of antimicrobials for the treatment of canine pyoderma in the UK." Thesis, Royal Veterinary College (University of London), 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618326.

Chiu, Chun-hung, and 趙俊雄. "The role of dynamic cooling in improving clinical efficacy during pulsed dye laser treatment of port wine stain in Chinese." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26661482.

Abreu, Marcelle Silva de. "Pioglitazone dosage forms for the treatment of inflammation associated with skin, ocular and neurodegenerative diseases." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/650877.

Giulbudagian, Michael [Verfasser]. "Development and Adaptation of Thermoresponsive Nanogels for the Treatment of Inflammatory Skin Diseases / Michael Giulbudagian." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1150238054/34.

Lee, Chuanfang. "An examination of British Chinese health care practice and beliefs : investigating the theory of planned behaviour, health-related quality of life, and Chinese medicine treatment for psoriasis." Thesis, University of Bath, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343766.

Ita, Kevin Bassey. "Skin delivery of selected hydrophilic drugs used in the treatment of skin diseases associated with HIV/AIDS by using elastic liposomes / Kevin Bassey Ita." Thesis, North-West University, 2003. http://hdl.handle.net/10394/302.

何慧潔. "嬰兒濕瘡的中醫治療和臨床研究現狀." HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1024.

Mahachi, Josia. "Medicinal properties of some plants used for the treatment of skin disorders in the O. R. Tambo and Amathole Municipalities of the Eastern Cape Province, South Africa." Thesis, Walter Sisulu University, 2013. http://hdl.handle.net/11260/101.

馮美金. "黃褐斑証治的文献整理與分析." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/976.

Mesquita, Pedro Miguel Amaral. "Psoríase: fisiopatologia e terapêutica." Master's thesis, [s.n.], 2013. http://hdl.handle.net/10284/4486.

李任時. "皮膚衰老的中西醫病機及治療的文獻整理與研究." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/969.

Försti, A. K. (Anna-Kaisa). "Incidence, mortality, comorbidities, and treatment of bullous pemphigoid in Finland." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526215310.

Kubin, M. (Minna). "Glucocorticoid receptors in inflammatory skin diseases:the effect of systemic and topical glucocorticoid treatment on the expression of GRα and GRβ." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526214023.

Bentley, Mary Jane. "Development and Evaluation of Disease Activity Measures in Rheumatoid Arthritis Using Multi-Level Mixed Modeling and Other Statistical Methodologies: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/461.

Sharifi, Bella. "The Development of Novel Apurinic/Aprymidinic Endonuclease/Redox-factor 1 Inhibitors for the Treatment of Human Melanoma." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/pharmaceutical_sciences_theses/7.

Johnson, Tim. "Heat treatments to control fungal skin diseases of potato." Thesis, University of Essex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435255.

Dawe, Robert Stewart. "Phototherapy in the treatment of skin disease in Scotland." Thesis, University of Glasgow, 2001. http://theses.gla.ac.uk/5857/.

Smith, Catherine Claire. "The mouse tail model in dermatology : a histological study on the effects of crude coal tar and isoquinoline." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236062.

Gånemo, Agneta. "Hereditary ichthyosis : Causes, Skin Manifestations, Treatments and Quality of Life." Doctoral thesis, Uppsala University, Dermatology and Venereology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-1780.

Hereditary ichthyosis is a collective name for many dry and scaly skin disorders ranging in frequency from common to very rare. The main groups are autosomal recessive lamellar ichthyosis, autosomal dominant epidermolytic hyperkeratosis and ichthyosis vulgaris, and x-linked recessive ichthyosis. Anhidrosis, ectropion and keratodermia are common symptoms, especially in lamellar ichthyosis, which is often caused by mutations in the transglutaminase 1 (TGM1) gene. The aim of this work was to study patients with different types of ichthyosis regarding (i) the patho-aetiology (TGM1 and electron microscopy [EM] analysis), (ii) skin signs and symptoms (clinical score and subjective measure of disease activity), (iii) quality of life (questionnaires DLQI, SF-36 and NHP and face-to-face interviews) and (iv) a search for new ways of topical treatment. Patients from Sweden and Estonia with autosomal recessive congenital ichthyosis (n=83) had a broader clinical spectrum than anticipated, but a majority carried TGM1 mutations. Based on DNA analysis and clinical examinations the patients were classified into three groups, which could be further subdivided after EM analysis. Our studies indicate that patients with ichthyosis have reduced quality of life as reflected by DLQI and by some domains of SF-36, by NHP and the interviews. All the interviewees reported that their skin disease had affected them negatively to varying degrees during their entire lives and that the most problematic period was childhood. All patients with ichthyosis use topical therapy. In a double-blind study creams containing either 5% urea or 20% propylene glycol were found inferior to a cream formulation containing lactic acid 5% and propylene glycol 20% both regarding clinical improvement and thinning of the skin barrier. Improved topical therapy may reduce the need of more toxic, oral drugs. Future studies should elucidate whether this increases the quality of life of ichthyosis patients, especially if combined with more detailed information about the aetiology and inheritance of the diseases.

Pfäffli, Daniel. "Molsidomine, a new drug for the treatment of coronary heart disease enhances PGI2 and PGE2 and inhibits thromoxane synthesis of cultured human skin fibroblasts /." [S.l : s.n.], 1985. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

ALMEIDA, SHIRLANE B. de. "Validação e avaliação dosimétrica empregando as técnicas de TL e OSL de materiais termoluminescentes para aplicação na dosimetria de feixes clínicos de elétrons utilizados na irradiação total da pele - TSI." reponame:Repositório Institucional do IPEN, 2017. http://repositorio.ipen.br:8080/xmlui/handle/123456789/27973.

Ruan, Xiumei. "Vehicle and enhancer effects on penetration of acyclovir through chicken and cockatiel skin in vitro." Thesis, 1992. http://hdl.handle.net/1957/37094.

Guiomar, Liliana Sofia Lima. "Evaluation of Humulus lupulus L. Therapeutic Properties for the Treatment of Skin Diseases." Master's thesis, 2020. http://hdl.handle.net/10400.6/10550.

Liang-ChengSu and 蘇良晟. "Transdermal delivery of drug-loaded particles using dissolvable microneedles for treatment of skin diseases." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sss2bw.

"Innovative options for the treatment of non-melanoma skin cancer : Investigations on the activity of antimicrobial peptides against topical diseases and study of peptide penetration into human skin ex vivo." Berlin : Freie Universität Berlin, 2014. http://d-nb.info/1063934591/34.

Nan, Lin Ming, and 林名男. "A Computer Simulation Model for the Disease Natural History and Cost-effectiveness Analysis for Prevention of Tuberculosis: Is It Worthwhile for Tuberculin Skin Test and Treatment of Latent Tuberculosis Infection in Taiwan?" Thesis, 2001. http://ndltd.ncl.edu.tw/handle/15603287374248245040.

Mosaics of predisposition cause skin disease

Clarifying the cause of a skin disease led to the discovery of a new disease-causing gene, a new category of diseases, and new perspectives for both counseling and therapy. The Kobe University discovery is the first time that epigenetic silencing, the "switching off" of an otherwise intact gene, has been recognized as the cause for a skin disease.

Porokeratosis is a skin disease that leads to the development of annular or circular, red and itchy lesions. In some individuals, these develop all over the body, in some localized in lines, and in some only in one or very few spots. Kobe University dermatologist KUBO Akiharu previously discovered that patients need "two hits" to one of the four genes that, when damaged, were known to cause the disease. Kubo explains: "We get one set of genes from each of our parents, which means that, for all genes relevant to this disease, we have two copies. However, patients had one deficient copy in all of their cells, which means that they inherited that from one of their parents. But they also had later mutations in the other copy in those areas of the skin where the disease developed." After that discovery, over fifty patients visited Keio University Hospital and Kobe University Hospital to get screened by Kubo, and among these they found eight patients who didn't have deficiencies in any of the four genes known to cause the disease, and they also had slightly different symptoms. "I was convinced that there is a yet unknown cause of porokeratosis, and so my graduate student at Keio University, SAITO Sonoko, started to search for it."

In The American Journal of Human Genetics , they now report that they identified a new gene, called FDFT1 , that when damaged will cause porokeratosis. But while those patients who had lesions all over the body had one deficient copy inherited from one of the parents and one later mutation in the affected cells, which is similar to what is known for other causative genes, those with more localized lesions did not have such an inherited damaged copy. Kubo says, "These observations led to the hypothesis that not genetic, but epigenetic changes in FDFT1 are hidden as the first hits." Epigenetic changes don't affect the DNA sequence that constitutes the gene but refer to molecular tags a cell can add to DNA to indicate whether or not to produce proteins from the gene, depending on the tag. "And that is exactly what we found. Epigenetic silencing of FDFT1 during early embryonic development in a cell that will give rise to skin cells is the first hit in this type of porokeratosis."

The whole picture therefore is that, in order to develop the disease, people need two damaged copies of the gene, "two hits." One they acquire either from their parents or through epigenetic silencing early in their fetal development. This predisposes them to developing the disease but in itself is not symptomatic. In the case of epigenetic silencing, individuals are mosaics of predisposed cells, which derive from the one where the silencing occurred, and unaffected cells. A second defect in the other copy of the gene leads to the development of the disease: Similar to the previously known causative genes, the protein produced from FDFT1 is involved in the production of cholesterol and with both copies of the gene deficient, toxic by-products accumulate in the cells.

These results have many fascinating implications. First, the knowledge of the affected gene allows doctors to prescribe a treatment. "We conducted a therapeutic evaluation of atorvastatin (a blocker of cholesterol production) and cholesterol ointment in three individuals with FDFT1 -deficient porokeratosis. All individuals exhibited reduced skin redness and thickening, pruritus (itchiness), and scaling within 4-12 weeks of treatment initiation, and no relapse was observed with continued use of the ointment," the researchers write in the paper. Second, when counseling patients, for those who didn't inherit a deficient gene from their parents it is reassuring news that they will also not pass the predisposition to the illness on to their children.

And third, "This is the first skin disease caused by early-development epigenetic silencing of a particular gene. Among all diseases, a fully comparable mechanism is only known in Lynch syndrome. We thus expect that epigenetic causes are hidden not only in these but also in other diseases, suggesting the existence of a category of diseases associated with the silencing of genes," the researchers explain.

• Epigenetics
• Gene Therapy
• Diseases and Conditions
• Human Biology
• Parkinson's Research
• Birth Defects
• Gene therapy
• Vector (biology)
• Huntington's disease
• Drug discovery
• Infectious disease

Story Source:

Materials provided by Kobe University . Note: Content may be edited for style and length.

Journal Reference :

• Sonoko Saito, Yuki Saito, Showbu Sato, Satomi Aoki, Harumi Fujita, Yoshihiro Ito, Noriko Ono, Takeru Funakoshi, Tomoko Kawai, Hisato Suzuki, Takashi Sasaki, Tomoyo Tanaka, Masukazu Inoie, Kenichiro Hata, Keisuke Kataoka, Kenjiro Kosaki, Masayuki Amagai, Kazuhiko Nakabayashi, Akiharu Kubo. Gene-specific somatic epigenetic mosaicism of FDFT1 underlies a non-hereditary localized form of porokeratosis . The American Journal of Human Genetics , 2024; DOI: 10.1016/j.ajhg.2024.03.017

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