Prof. Roberto Grobman
Keywords: skin, genetics, algorithms, biomarkers, wrinkles, aging,
artificial intelligence
Abstract
Introduction: 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.
Introduction
The 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 results
Bioinformatics 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 .