Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum

Busch DA, Richter ML, Hüsers J, Przysucha M, Kücking F, Mang JM, Moelleken M, Dissemond J, Heggemann J, Hafer G, Berking C, Erfurt-Berge C, Hübner UH (2025)

Publication Type: Journal article

Publication year: 2025

Journal

BMJ Health & Care Informatics BMJ Publishing Group

Book Volume: 32

Article Number: e101418

Journal Issue: 1

DOI: 10.1136/bmjhci-2024-101418

Abstract

Study objectives Chronic wounds represent a significant economic and personal burden. For their successful treatment, the causes must be known and treated. Wounds caused by pyoderma gangrenosum (PG), a rare inflammatory skin disease, are often misdiagnosed. This study, therefore, aims to develop a machine learning model capable of differentiating PG from other wound types, focusing on chronic leg wounds to address this diagnostic challenge. Methods We used 3674 wound photographs from three specialised wound centres with the four most common types of foot and leg ulcers and the rare inflammatory differential diagnosis PG. The convolutional neural network classifier ConvNeXt ‘B’ was pretrained on LAION2B, ImageNet12k and ImageNet 1k before being trained and fine-tuned on an 85:15 train, validation split. Results The model achieved an overall high accuracy in multiclass classification of the chronic wounds (unbalanced accuracy 90%, balanced accuracy 87%). The sensitivity for identifying PG was 94%, while the sensitivity forother chronic wound types was 97% for diabetic foot ulcers (DFU), 92% for venous leg ulcers (VLU), 78% for mixed leg ulcers and 74% for arterial leg ulcers. Discussion The machine learning model effectively differentiates PG from the most common leg and foot ulcers and was very accurate for classifying DFU and VLU. A higher rate of misclassifications occurred for the other vascular ulcers, that is, mixed and arterial leg ulcers. This aligns with the challenges in clinical practice. Conclusion Despite the limited number of wound images, this novel multiclass wound classification model accurately identified PG and differentiated leg and foot ulcer subtypes, providing a foundation for a diagnostic support system.

Authors with CRIS profile

Jonathan Mang Medizinisches Zentrum für Informations- und Kommunikationstechnik (Medizinisches IK-Zentrum, MIK) Carola Berking Lehrstuhl für Haut- und Geschlechtskrankheiten Cornelia Erfurt-Berge Department of Dermatology

Involved external institutions

Hochschule Osnabrück DE Germany (DE) Quebec AI Institute / Quebec Artificial Intelligence Institute / Montreal Institute for Learning Algorithms (MILA) CA Canada (CA) Universitätsklinikum Essen DE Germany (DE) Niels-Stensen-Kliniken GmbH DE Germany (DE)

How to cite

APA:

Busch, D.A., Richter, M.L., Hüsers, J., Przysucha, M., Kücking, F., Mang, J.M.,... Hübner, U.H. (2025). Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum. BMJ Health & Care Informatics, 32(1). https://doi.org/10.1136/bmjhci-2024-101418

MLA:

Busch, Dorothee A., et al. "Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum." BMJ Health & Care Informatics 32.1 (2025).

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