Clinical applications of machine learning for infection assessment in diabetic foot ulcers

TL;DR

Machine learning can help detect infections in diabetic foot ulcers by analyzing images, potentially improving early diagnosis and reducing amputation risks.

Contribution

This paper reviews recent advances in using machine learning for infection assessment in diabetic foot ulcers and highlights their clinical potential.

Findings

ML-based systems show encouraging performance in identifying infection patterns in DFU images.

Current limitations include dataset bias and lack of standardized imaging protocols.

ML tools can support telemedicine and remote monitoring for DFU management.

Abstract

Diabetic foot ulcers (DFUs) represent one of the most severe complications of diabetes mellitus and are frequently complicated by infection, which significantly increases the risk of hospitalization, lower-limb amputation, and mortality. Early and accurate detection of infection in DFUs is therefore critical; however, clinical assessment remains challenging and is largely based on subjective visual evaluation. Inter-observer variability, atypical inflammatory responses in patients with diabetes, and inconsistent wound documentation contribute to delayed or inaccurate diagnoses. In recent years, digital sound imaging combined with machine learning (ML) techniques has emerged as a promising adjunct to traditional clinical assessment. This review summarizes and critically evaluates recent advances in the application of ML for infection assessment in DFUs. We review image-based ML approaches designed to detect infection-related visual features, including erythema, purulent exudate, necrosis, and tissue discoloration, as well as models developed for ulcer classification, tissue segmentation, and longitudinal wound monitoring. In addition, we discuss the clinical utility of ML-assisted tools in telemedicine, remote monitoring, and decision support, particularly in community and resource-limited settings. Current limitations, including image variability, dataset bias, lack of standardized imaging protocols, and limited clinical validation, are also addressed. Overall, ML-based systems have demonstrated encouraging performance in identifying infection-associated patterns in DFU images and may help reduce diagnostic variability and support earlier clinical intervention. Nevertheless, further large-scale prospective studies, regulatory validation, and integration into clinical workflows are required before widespread adoption. Machine learning should be viewed as a supportive tool that complements, rather than replaces, clinical expertise in the management of diabetic foot infections.

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