Preventing diabetic foot ulcers in low resource settings using Pedal Elevated Temperature Risk Assessment (PETRA)

Abstract

AbstractDiabetic foot ulcers develop for up to 1 in 3 patients with diabetes. While ulcers are costly to manage and often necessitate an amputation, they are preventable if intervention is initiated early. However, with current standard of care, it is difficult to know which patients are at highest risk of developing an ulcer. Recently, thermal monitoring has been shown to catch the development of complications around 35 days in advance of onset. We seek to use thermal scans of patients’ with diabetes feet to automatically detect and classify a patient’s risk for foot ulcer development so that intervention may be initiated. We began by comparing performance of various architectures (backbone: DFTnet, ResNet50, and Swin Transformer) trained on visual spectrum images for monofilament task. We moved forward with the highest accuracy model which used ResNet50 as backbone (DFTNet acc. 68.18%, ResNet50 acc. 81.81%, Transformers: acc. 72.72%) to train on thermal images for the risk prediction task and achieved 96.4% acc. To increase interpretability of the model, we then trained this same architecture to predict two standard of care risk scores: high vs low-risk monofilament scores (81.8% accuracy) and high vs low-risk biothesiometer score (77.4% accuracy). We then sought to improve performance by facilitating the model’s learning. By annotating feet bounding boxes, we trained our own YoloV4 detector to automatically detect feet in our images (mAp accuracy of 99.7% and IoU of 86.%). By using these bounding box predictions as input to the model, this improved performance of our two classification tasks: MF 84.1%, BT 83.9%. We then sought to further improve the accuracy of these classification tasks with two further experiments implementing visual images of the feet: 1) training the models only on visual images (Risk: 97.6%, MF: 86.3%, BT: 80.6%), 2) concatenating visual images alongside the thermal images either early (E) or late (L) fusion in the architecture (Risk, E: 99.4%, L: 98.8% ; MF, E: 86.4%, L: 90.9%; BT, E: 83.9%, L: 83.9%). Our results demonstrate promise for thermal and visible spectrum images to be capable of providing insight to doctors such that they know which patients to intervene for in order to prevent ulceration and ultimately save the patient’s limb.