Validation of a Trained AI Imaging Model for Detecting Diabetic Foot Deformities

Diabetes is a leading cause of morbidity and mortality, contributing to complications such as cardiovascular disease, kidney failure and lower-limb amputations. Diabetic foot complications, such as structural deformities, ulceration and infection, present significant risks, necessitating early detection and intervention. This study explores the development and validation of artificial intelligence (AI) image analysis for diabetic foot screening, focusing on structural deformity identification which includes callus, hallux valgus and hammer toes, because they represent the earliest detectable visual risk markers for ulceration, preceding wound formation. Leveraging datasets comprising over 1000 healthy foot images and 215 diabetic foot deformity images, the model employed YOLOv5 for object detection, a convolutional autoencoder for anomaly detection, and DenseNet201 for anomaly classification. Initial internal validation yielded 91.1% anomaly detection accuracy, while anomaly classification accuracy improved to 88.57% following refinement. External validation using 27 participants achieved an overall accuracy of 85.2% and anomaly classification accuracy of 66.7%. Final evaluation on 35 unlabelled images demonstrated promising performance, with 88.57% accuracy, 90.47% precision and an F1 score of 86.11%. Integrated into the 'Foot at Risk' (FAR) mobile application, this AI-driven solution offers a scalable tool for early diabetic foot deformity detection. With larger dataset input for training and development, it can be utilised as an early screening tool for diabetic foot and integrated into existing community diabetic care model, facilitating timely intervention and improving patient outcomes.