Automated Prediction of Diabetes-Related Foot Ulcer Healing at Initial Assessment

BACKGROUND: Diabetes-related foot ulcers (DFUs) that fail to heal within the expected trajectory and become chronic are associated with substantial morbidity. The current clinical standard for identifying chronic DFUs relies on a <50% reduction in wound area over 4 weeks. There is a critical need for tools that can predict wound-healing outcomes at first clinical presentation to enable timely, individualised wound management. We have developed and validated TherMUL, an AI device that analyses the texture of thermal images to predict DFU healing. METHODS: Adults with neuropathic DFUs were recruited from 2 specialist foot clinics in Melbourne, Australia. TherMUL, a software-based device, was used to take thermal images during the initial visit. Texture features were extracted using grey-level co-occurrence matrix (GLCM) analysis. Wound-healing status at 12 weeks served as the ground-truth label for model training. A support vector machine (SVM) classifier was trained using the images from 44 healed and 24 unhealed DFUs. A separate unseen dataset was used for a preliminary feasibility assessment. RESULTS: The classifier achieved a mean area under the curve (AUC) of 0.635, with a sensitivity of 75.8% for predicting unhealed DFUs, and a specificity of 53% for identifying healed DFUs. In the preliminary evaluation, 5 of 7 predictions correctly matched the 12-week podiatry outcomes. CONCLUSION: This study demonstrated early feasibility of using TherMUL to assist clinicians in predicting 12-week DFU healing outcomes at first clinical presentation, with moderate discriminative performance. This non-contact, point-of-care approach has the potential to support earlier identification of DFUs unlikely to heal and enable more timely, targeted wound management strategies.