Skin lesions are one of the most common human diseases and affect millions of people worldwide 1. Moreover, its prevalence will raise every year due to the aging of the population; studies reveal that it increases to 50% in patients over 65 years, with an average of more than two skin diseases per elder 2. The issue becomes more critical if we consider that the incidence of skin cancer also increases with age. The clinically significant changes of the skin are as varied as the diseases and disorders themselves and may include infections by bacteria, viruses or mold, exposure to allergens or toxins, inherited genetic conditions, immune system disorders, trauma and cancer. The diversity of dermatological diseases, as well as their symptoms and causes, create challenges for the objective diagnosis and evaluation of patient lesions on the daily clinical practice.
Diagnosis in dermatology is largely based on visual inspection of a lesion or the suspicious skin area. Therefore, diagnostic ability and accuracy depends greatly on the experience and training of dermatologists or, in areas where dermatological services are not readily available, general practitioners. For example, dermatologists have approximately 70% accuracy in the visual diagnosis of melanoma 3. In suspicious cases, the visual inspection is supplemented with different diagnostic tools (e.g. dermoscopy). Even with this technical support, dermatologists rarely achieve clinical test sensitivities greater than 85% 4 5 . The situation is even worse if we consider that there is a shortage of dermatologists and the diagnostic accuracy of non-expert clinicians is not as good as that of dermatologists, ranging from 20 to 40% according to different studies 6-8 . Thus, new diagnostic tools to assist dermatologists or general practitioners to accurately diagnose skin lesions should be developed, evaluated and optimized.
As diagnosis of a skin lesion is mainly based on its visual inspection, the use of artificial intelligence (AI) for this task is a growing trend in dermatology. Particularly, computer vision, a subfield of AI that mimic tasks performed by the human visual system, allows the rapid reviewing and classification of immense amounts of images. In dermatology, image recognition using a set of computer vision algorithms called deep convolutional neural networks (CNNs) prove to be a significant aid to physicians for diagnosis of melanoma, with accuracies comparable to those of expert dermatologists 9-14. Similar results were also found for non-pigmented skin lesions such as acne, rosacea, psoriasis, atopic dermatitis or impetigo. Moreover, a recent work showed the results of a deep learning system able to classify 26 of the most common skin conditions 15.
Thus, these technologies show tremendous potential to improve the ability of dermatologists and general practitioners to correctly diagnose a skin lesion. Some of these classifiers have been translated onto online platforms and smartphone applications 16. However, many aspects of their use have yet to be elucidated and improved. Algorithms’ decisions are usually defined on a small subset of disease classes and therefore they do not reflect a clinical practice that include many more diagnostic options 17. In addition, computer vision models combine pixel-based visual information in a highly intricate way, making it difficult to link the output of the model back to the visual input, needing extensive validation. Lastly, the efficacy of CNNs depends largely on the complexity and number of images utilized for their training, raising concerns about their ability to generalize on a truly external test set coming from a different image database. For example, an image dataset composed mainly of skin images of a fair-skinned Caucasian population cannot be extrapolated to other racial groups. Because of all these caveats, the use of these classification algorithms should be regarded as an aid for dermatologists or general practitioners but not as a tool for automatic diagnoses without a supervising physician.
At any rate, a digital skin assistance tool should probably provide an undeniable help for dermatologists to reduce the morbidity and mortality of dermatological diseases by favoring early diagnosis and avoiding unnecessary procedures. Moreover, this technology would help primary care physicians to decide which patients are at highest risk, for example, of skin cancer, recommending an urgent referral to a dermatologist for confirmation and for total body skin examination. These models could be easily implemented on a website, mobile app, tele-dermatology platform or even integrated into an electronic medical record system enabling fast and cheap access to skin screenings, even outside hospitals or medical centers. In the end, automated diagnostic systems using AI would allow clinicians to spend more time conducting work in line with their skills and training, with the assistance of AI tools, with the final goal of improving patient care in a routine clinical setting.
Carlos M. Galmarini
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