Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/56833
Title: Dermoscopic Features of Cutaneous Vasculitis
Authors: Bakay, O.S.K.
Kacar, N.
Gonulal, M.
Demirkan, N.C.
Cenk, H.
Goksin, S.
Gural, Y.
Keywords: cutaneous vasculitis
Dermoscopy
inflammoscopy
machine learning
Publisher: Mattioli 1885
Abstract: Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. ABSTRACT Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis. Objectives: We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods. Methods: Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models. Results: The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (κ = 0.869) and background color analysis (κ = 0.846) (P < 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV. Conclusions: Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy. © 2024 Karstarli Bakay et al.
URI: https://doi.org/10.5826/dpc.1401a51
https://hdl.handle.net/11499/56833
ISSN: 21609381
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Tıp Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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