Prof. Roberto Grobman Keywords: skin, genetics, algorithms, biomarkers, wrinkles, aging, artificial intelligenceAbstractIntroduction: Skin, being the largest organ system in the body, is of utmost importance when it comes to timely diagnostics and treatment of skin conditions. Diagnostics, in history, have been dependent on symptoms and the doctor’s experience. Today, with advances in technology, is it possible to diagnose skin conditions more accurately and early. Skin imaging and deep learning have contributed immensely in very early diagnosis and hence a better prognosis. Artificial intelligence (AI) techniques have been applied in clinical genomics to identify genetic markers for predisposed conditions such as melanoma, psoriasis etc.Methods and results: Research and analysis of three studies were performed to obtain collective data on the current trends in skin disease diagnosis and mapping of genetic markers. AI shows a lot of promise in prediction of skin conditions and early treatment.Conclusion: Skin disease prognosis has been improved by the use of skinomics, microarray and AI techniques for accurate diagnostics and treatment.IntroductionThe skin is the largest organ of the body, composed of epidermis, dermis, and subcutaneous tissues, containing blood vessels, lymphatic vessels, nerves, and muscles, which can perspire, perceive the external temperature, and protect the body. Covering the entire body, the skin can protect multiple tissues and organs in the body from external invasions including artificial skin damage, chemical damage, adventitious viruses, and individuals’ immune system . Skin diseases have a big impact on everyday life and detecting underlying issues at the earliest is gaining importance. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases.Skin diseases and conditions are extremely prevalent, yet diagnostics are based on symptoms and the experience of the doctor. These are, often, not fool-proof and sometimes require a trial-and-error approach to diagnosis. Over the past few years, the image processing technique has achieved rapid development in medicine . A great example, the skin disease varicella was detected by Oyola and Arroyo through image processing technique’s colour transformation, equalization as well as edge detection, and the image of varicella was eventually collected and classified through Hough transform. The final empirical results demonstrated that a better diagnosis was received in terms of detection on varicella, and preliminary test was also conducted on varicella and herpes zoster on that basis. Sumithra et al. proposed a novel approach for automatic segmentation and classification of skin lesions by using SVM and k-nearest neighbor (k-NN) classifier. Kumar and Singh [20] established the relationship of skin cancer images across different types of neural network. Then, medical images were collected into this skin cancer classification system for training and testing based on the matlab image processing toolbox .Bioinformatics is a research field that uses computer‐based tools to investigate life sciences questions, employing “big data” results from large‐scale DNA sequencing, whole genomes, transcriptomes, metabolomes, populations, and biological systems, which can only be comprehensively viewed in silico. The epidermis was among the earliest targets of bioinformatics studies because it represents one of the most accessible targets for research. Consequently, bioinformatics methods in the fields of skin biology and dermatology generated a large volume of bioinformatics data, which led to origination of the term “skinomics.” Skinomics data are directed toward epidermal differentiation, malignancies, inflammation, allergens, and irritants, the effects of ultraviolet (UV) light, wound healing, the microbiome, stem cells, etc. Cultures of cutaneous cell types, keratinocytes, fibroblasts, melanocytes, etc., as well as skin from human volunteers and from animal models, have been extensively experimented on . We are presenting some combined research information on diagnostic imaging and application of bioinformatics in skin diseases through this article.Methods and resultsBioinformatics is an interdisciplinary field of knowledge that combines computer science, biology and biomedical sciences and statistics. Bioinformatics is oriented to the application and development of new computational methods to expand biological, biomedical or epidemiological knowledge.We used a data set provided by Transceptar Technologies/FullDNA, from Israel. The algorithm developed by Transceptar Technologies TRCPR18 has AI-based technology and allows the analysis of millions of data in a few seconds, taking into account the orientation of the gene and proceeding with various types of predisposition calculations. The Transceptar / FullDNA algorithm analyzes more than 61 skin-related conditions and this information was used to confirm previous research.Recent developments in high-speed technologies have led to a major revolution in biological and biomedical research and where today bioinformatics plays an increasingly central role in the analysis of large amounts of data.Literature from three studies were researched to summarise modern advances in skin disease diagnostics using Artificial Intelligence (AI), bioinformatics, skin imaging and machine learning.Imaging and deep learning applications:A study conducted by Patnaik et al. researched an approach to use various computer vision based techniques (deep learning) to automatically predict the various kinds of skin diseases. The system uses three publicly available image recognition architectures namely Inception V3, Inception Resnet V2, Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. The study approach involved development of a widespread plan to test the special features and general functionality on a range of platform combination, initiated by the test process. The method involves use of pre-trained image recognizers with modifications to identify skin images. The use of deep learning and ensembling features, results showed higher accuracy rate along with identification of more diseases. Previous models reported a maximum of six skin diseases with an accuracy level of 75% compared to as many as twenty diseases with an accuracy of 88%, in the study conducted by Patnaik et al. This proves that deep learning algorithms have a huge potential in the real world skin disease diagnosis .Microarray and skinomics applications:The most commonly used and highly preferred methodology in skinomics is DNA microarray technology, such as Affymetrix and Illumina. DNA microarrays are a perfect medium as they simultaneously measure the expression of the entire genome . Printed cDNA arrays, originated by Brown at Stanford , are often homemade, inexpensive, and can compare two samples on the same chip. Commercial alternatives such as oligonucleotide microarrays are available too, but a little expensive. These techniques offer personalized medication and find broad applications in the future. Microarray technology can be applied in skin ageing studies, UV damage studies, transcriptional studies in melanoma and wound healing studies. Genome‐wide association studies, GWAS, comprise examination of many common DNA polymorphisms in a large population cohort to detect association of polymorphisms with a given disease. Such polymorphisms can point to the genes where disease‐causing mutations may map. GWAS are particularly useful in the analysis of diseases, such as psoriasis, which are common and with a strong genetic component .Artificial intelligence in clinical genomics:Most artificial intelligence techniques have been adapted to address the various steps involved in clinical genomic analysis—including variant calling, genome annotation, variant classification, and phenotype-to-genotype correspondence—and perhaps eventually they can also be applied for genotype-to-phenotype predictions . AI has proven to be highly effective in the following areas:Variant Calling : The clinical interpretation of genomes is sensitive to the identification of individual genetic variants among the millions populating each genome, necessitating extreme accuracy. Standard variant-calling tools are prone to systematic errors that are associated with the subtleties of sample preparation, sequencing technology, sequence context, and the sometimes unpredictable influence of biology such as somatic mosaicism . AI algorithms can learn these biases from a single genome with a known gold standard of reference variant calls and produce superior variant calls .Phenotype-to-genotype mapping : The molecular diagnosis of skin disease often requires both the identification of candidate pathogenic variants and a determination of the correspondence between the diseased individual’s phenotype and those expected to result from each candidate pathogenic variant. AI algorithms can significantly enhance the mapping of phenotype to genotype, especially through the extraction of higher-level diagnostic concepts that are embedded in medical images and EHRs .Genotype-to-phenotype prediction : The ultimate purpose of clinical genetics is to provide diagnoses and forecasts of future disease risk. Although, not many successful predictions have been made in literature yet, this shows promise in the fact that a few simple studies have shown to accurately predict conditions .Conclusion:AI systems have surpassed the performance of state-of-the-art methods and have gained FDA clearance for a variety of clinical diagnostics, especially imaging-based diagnostics. The availability of large datasets for training, together with advances in AI algorithms is driving this surge of productivity. Deep-learning algorithms have shown tremendous promise in a variety of clinical genomics tasks such as variant calling, genome annotation, and functional impact prediction. It is possible that more generalized AI tools will become the standard in these areas, especially for clinical genomics tasks where inference from complex data is a frequently recurring task .The application of AI in medicine is a burgeoning area of development in light of the major impact it could potentially have on healthcare provision. The application of machine learning in medical imaging on skin lesions has been the most impactful, and demonstrates the potential for this technology in medical practice .
