Dermoscopic Features of Cutaneous Vasculitis
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 increa...
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| Format: | Article |
| Language: | English |
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Mattioli1885
2024-01-01
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| Series: | Dermatology Practical & Conceptual |
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| Online Access: | https://dpcj.org/index.php/dpc/article/view/3583 |
| _version_ | 1797324043390550016 |
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| author | Ozge Sevil Karstarli Bakay Nida Kacar Melis Gonulal Nese Calli Demirkan Hülya Cenk Sule Goksin Yunus Gural |
| author_facet | Ozge Sevil Karstarli Bakay Nida Kacar Melis Gonulal Nese Calli Demirkan Hülya Cenk Sule Goksin Yunus Gural |
| author_sort | Ozge Sevil Karstarli Bakay |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-08T05:44:33Z |
| format | Article |
| id | doaj.art-3321c96dba3641198e6afd88e90b3b76 |
| institution | Directory Open Access Journal |
| issn | 2160-9381 |
| language | English |
| last_indexed | 2024-03-08T05:44:33Z |
| publishDate | 2024-01-01 |
| publisher | Mattioli1885 |
| record_format | Article |
| series | Dermatology Practical & Conceptual |
| spelling | doaj.art-3321c96dba3641198e6afd88e90b3b762024-02-05T13:15:28ZengMattioli1885Dermatology Practical & Conceptual2160-93812024-01-0114110.5826/dpc.1401a51Dermoscopic Features of Cutaneous VasculitisOzge Sevil Karstarli Bakay0Nida Kacar1Melis Gonulal2Nese Calli Demirkan3Hülya Cenk4Sule Goksin5Yunus Gural6Pamukkale University Faculty of Medicine, Department of Dermatology, Denizli, TurkeyPamukkale University Faculty of Medicine, Department of Dermatology, Denizli, TurkeyTepecik Education and Research Hospital Department of Dermatology, University of Health Sciences Turkey, İzmir, TurkeyDepartment of Pathology, Medical Faculty, Pamukkale University, Denizli, TurkeyPamukkale University Faculty of Medicine, Department of Dermatology, Denizli, TurkeyPamukkale University Faculty of Medicine, Department of Dermatology, Denizli, TurkeyFirat University Faculty of Science, Division of Statistics, Elazig, Turkey 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. https://dpcj.org/index.php/dpc/article/view/3583dermoscopymachine learningcutaneous vasculitisinflammoscopy |
| spellingShingle | Ozge Sevil Karstarli Bakay Nida Kacar Melis Gonulal Nese Calli Demirkan Hülya Cenk Sule Goksin Yunus Gural Dermoscopic Features of Cutaneous Vasculitis Dermatology Practical & Conceptual dermoscopy machine learning cutaneous vasculitis inflammoscopy |
| title | Dermoscopic Features of Cutaneous Vasculitis |
| title_full | Dermoscopic Features of Cutaneous Vasculitis |
| title_fullStr | Dermoscopic Features of Cutaneous Vasculitis |
| title_full_unstemmed | Dermoscopic Features of Cutaneous Vasculitis |
| title_short | Dermoscopic Features of Cutaneous Vasculitis |
| title_sort | dermoscopic features of cutaneous vasculitis |
| topic | dermoscopy machine learning cutaneous vasculitis inflammoscopy |
| url | https://dpcj.org/index.php/dpc/article/view/3583 |
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