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Artificial Intelligence in the Detection and Classification of Skin Diseases and Skin Cancer

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Skin Health".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2672

Special Issue Editors


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Guest Editor
Department of Dermatology and Allergy, University Hospital, LMU Munich, 80336 Munich, Germany
Interests: dermatology; dermatosurgery; dermato-oncology; melanoma and non-melanoma skin cancer; non-invasive diagnostics in dermatology; dermatopathology
Special Issues, Collections and Topics in MDPI journals

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Assistant Guest Editor
Department of Dermatology and Allergy, University Hospital, LMU Munich, 80336 Munich, Germany
Interests: dermatology; dermatosurgery; dermato-oncology; melanoma and non-melanoma skin cancer; non-invasive diagnostics in dermatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has started changing our perspectives in medical diagnostics since the introduction and development of convolutional neural networks, advanced machine learning, and deep learning approaches. Expert dermatologists have been used to diagnose diseases based on visual inspection, evaluating morphological parameters such as colors, shapes, and borders. AI uses computer systems and algorithms to mimic the cognitive functions of the human brain and to support the work of researchers and clinicians. In the last few years, there has been an enormous increase in the applications of AI in dermatology, for the detection and classification of skin cancer but also other skin diseases on all levels. Future challenges include the improvement of the diagnostic performance of AI systems for skin diseases, their standardization, the extension of the disease spectrum, and integration in daily clinical practice.

This Special Issue welcomes research papers, short communications, and reviews focusing on the latest advances in the use of AI approaches for the detection and severity grading of skin cancer, inflammatory skin diseases, and skin pathophysiology (skin aging, skin hydration) from the clinical, epidemiological, technical, and histopathological point of view.

Dr. Daniela Hartmann
Guest Editor

Dr. Cristel Ruini
Assistant Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • machine learning
  • skin cancer
  • skin aging
  • inflammatory skin diseases
  • non-invasive and innovative diagnostic technologies in dermatology

Published Papers (1 paper)

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Research

8 pages, 886 KiB  
Article
Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population
by Manuel Martin-Gonzalez, Carlos Azcarraga, Alba Martin-Gil, Carlos Carpena-Torres and Pedro Jaen
Int. J. Environ. Res. Public Health 2022, 19(7), 3892; https://doi.org/10.3390/ijerph19073892 - 24 Mar 2022
Cited by 5 | Viewed by 1930
Abstract
(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma in a hospital population. [...] Read more.
(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma in a hospital population. (2) Methods: A retrospective study was performed using 232 dermoscopic images from the clinical database of the Ramón y Cajal University Hospital (Madrid, Spain). The skin lesions images, previously diagnosed as nevus (n = 177) or melanoma (n = 55), were analyzed by the quantusSKIN system, which offers a probabilistic percentage (diagnostic threshold) for melanoma diagnosis. The optimum diagnostic threshold, sensitivity, specificity, and accuracy of the quantusSKIN system to diagnose melanoma were quantified. (3) Results: The mean diagnostic threshold was statistically lower (p < 0.001) in the nevus group (27.12 ± 35.44%) compared with the melanoma group (72.50 ± 34.03%). The area under the ROC curve was 0.813. For a diagnostic threshold of 67.33%, a sensitivity of 0.691, a specificity of 0.802, and an accuracy of 0.776 were obtained. (4) Conclusions: The quantusSKIN system is proposed as a useful screening tool for melanoma detection to be incorporated in primary health care systems. Full article
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