The Role of Machine Learning in Infection Assessment of Diabetic Foot Ulcers: A Narrative Review
Summary: This 2026 narrative review critically evaluates machine learning (ML) applications for detecting infection in diabetic foot ulcers (DFUs), a major cause of hospitalization, amputation, and mortality in diabetes. Clinical assessment relies on subjective visual signs (redness, swelling, purulence), but inter-observer variability, atypical responses in neuropathy/ischemia, and poor documentation lead to delays or misdiagnosis. ML, especially deep learning on wound images, detects subtle infection features (erythema, exudate, necrosis, discoloration) with high accuracy. Covers image-based classification (infected vs. uninfected), tissue segmentation (necrotic vs. granulation), longitudinal monitoring, and prognostic models for healing/amputation risk. Highlights utility in telemedicine, remote monitoring, and resource-limited settings. Limitations: Image variability, dataset bias, lack of standardized protocols, limited prospective validation. Encourages ML as a supportive tool to complement clinical expertise, not replace it; calls for large-scale studies, regulatory approval, and workflow integration to reduce diagnostic errors and enable earlier intervention in DFIs.
Key Highlights:
- ML excels at image-based infection detection and classification (e.g., >90% accuracy in some models)
- Supports segmentation, monitoring, and prognosis in DFUs
- Benefits telemedicine and resource-limited care
- Limitations: Bias, variability, need for validation
- Relevance: Reduces subjectivity in chronic diabetic wound infection assessment
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Keywords: machine learning DFU, infection assessment, diabetic foot ulcer, telemedicine wound