Dermoscopic Features of Cutaneous Vasculitis

Dermoscopic Features of Cutaneous Vasculitis

Authors

  • Ozge Sevil Karstarli Bakay Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, Turkey
  • Nida Kacar Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, Turkey
  • Melis Gonulal Tepecik Education and Research Hospital Department of Dermatology, University of Health Sciences Turkey, İzmir, Turkey
  • Nese Calli Demirkan Department of Pathology, Medical Faculty, Pamukkale University, Denizli, Turkey
  • Hülya Cenk Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, Turkey
  • Sule Goksin Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, Turkey
  • Yunus Gural Firat University Faculty of Science, Division of Statistics, Elazig, Turkey

Keywords:

dermoscopy, machine learning, cutaneous vasculitis, inflammoscopy

Abstract

Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. 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 (???? = 0.869) and background color analysis (???? = 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.

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Published

2024-01-31

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How to Cite

1.
Dermoscopic Features of Cutaneous Vasculitis. Dermatol Pract Concept [Internet]. 2024 Jan. 31 [cited 2024 Jul. 13];14(1):e2024051. Available from: https://dpcj.org/index.php/dpc/article/view/3583

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