ArticleJournal of clinical medicine2022
Computer-Assisted Differential Diagnosis of Pyoderma Gangrenosum and Venous Ulcers with Deep Neural Networks.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Clinical Decision-Making and Multidisciplinary Management of Peristomal Pyoderma Gangrenosum in Stage IVB Rectal Cancer: A Case Report-Corticosteroid Response but Fatal Cancer Progression.Reports (MDPI) · 2026Article
- Convolutional Neural Networks in Chronic Wound Segmentation and Tissue Classification Using Real-World Images.International wound journal · 2026Article
- Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum.BMJ health & care informatics · 2025Article
- Diagnostic accuracy differences in detecting wound maceration between humans and artificial intelligence: the role of human expertise revisited.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Clinical characteristics, treatment, and wound management of pyoderma gangrenosum: A case series.PloS one · 2025Article
- Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.Dermatology and therapy · 2024Article
- Narrative Review: Pyoderma Gangrenosum.Cureus · 2024Review
- Real-world use of a deep convolutional neural network to assist in the diagnosis of pyoderma gangrenosum.JAAD case reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Pyoderma gangrenosum (PG) is often situated on the lower legs, and the differentiation from conventional leg ulcers (LU) is a challenging task due to the lack of clear clinical diagnostic criteria. Because of the different therapy concepts, misdiagnosis or delayed diagnosis bears a great risk for patients. (2) Objective: to develop a deep convolutional neural network (CNN) capable of analysing wound photographs to facilitate the PG diagnosis for health professionals. (3) Methods: A CNN was trained with 422 expert-selected pictures of PG and LU. In a man vs. machine contest, 33 pictures of PG and 36 pictures of LU were presented for diagnosis to 18 dermatologists at two maximum care hospitals and to the CNN. The results were statistically evaluated in terms of sensitivity, specificity and accuracy for the CNN and for dermatologists with different experience levels. (4) Results: The CNN achieved a sensitivity of 97% (95% confidence interval (CI) 84.2−99.9%) and outperformed dermatologists, with a sensitivity of 72.7% (CI 54.4−86.7%) significantly (p < 0.03). However, dermatologists achieved a slightly higher specificity (88.9% vs. 83.3%). (5) Conclusions: For the first time, a deep neural network was demonstrated to be capable of diagnosing PG, solely on the basis of photographs, and with a greater sensitivity compared to that of dermatologists.
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