Rashi Pangti

Citation: Pangti R, Yadav A, Mendiratta V. Plica Polonica Secondary to Chemotherapeutic Drugs Combination. Dermatol Pract Concept. 2023;13(1):e2023061. DOI: https://doi.org/10.5826/dpc.1301a61 Accepted: July 6, 2022; Published: January 2023 Copyright: ©2023 Pangti et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original authors and source are credited. Funding: None. Competing interests: None. Authorship: All authors have contributed significantly to this publication. Corresponding author: Rashi Pangti, Assistant Professor, Department of Dermatology & STD, Lady Hardinge Medical College and associated hospitals, New Delhi-110001, India. Phone: +91-9990226358 Email: rashipangti457@gmail.com This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Citation: Pangti R, Khan I, Gupta S. Vitiligo co-localized over a patch of alopecia areata in two patients: more than a coincidence? Dermatol Pract Concept. 2022;12(1):e2022007. DOI: https://doi.org/10.5826/dpc.1201a07 Accepted: April 25, 2021; Published: January 2022 Copyright: ©2022 Pangti et al. This is an open-access article distributed under the terms of the Creative Commons Attribution- NonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted noncommercial use,distribution, and reproduction in any medium, provided the original authors and source are credited. Funding: None. Competing interests: None. Authorship: All authors have contributed significantly to this publication. Corresponding author: Somesh Gupta, Professor, Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India. E-mail: someshgupta@hotmail.com This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

During the COVID‐19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID‐19‐related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID‐19‐associated skin lesions from clinical images. An ensemble model of three different CNN‐based algorithms was trained with clinical images of skin lesions from confirmed COVID‐19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID‐19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi‐class model demonstrated an overall top‐1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID‐19‐rash detection were found to be 84.2 ± 5.1% and 99.5 ± 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID‐19‐rash were 88.0 ± 5.6%, 99.4 ± 0.2% and 0.97 ± 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 ± 6.25%, 99.77 ± 0.14% and 98.91 ± 0.17%, respectively for COVID‐19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID‐19‐associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning‐based study for automated detection of COVID‐19 based on skin images and may provide a useful decision support tool for physicians to optimize contact‐free COVID‐19 triage, differential diagnosis of skin lesions and patient care.