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

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

SUBMITTER: Busch DA 

PROVIDER: S-EPMC12517012 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature