Monitoring Diabetic Foot Using Images and Generative AI

Background: Diabetic Foot Ulcers (DFU) are a common complication of diabetes, often leading to infections, amputations and even death if left untreated. Effective management of the Diabetic Foot (DF) requires timely detection and frequent monitoring. Current DF assessment methods, by healthcare professionals, are largely based on visual inspection of feet, together with touch, temperature, and vibration sensitivity, and pedal pulse. Methods: The paper describes a machine-learning approach for the assessment of DF from feet images, combining pre-trained convolutional neural networks (CNN) with Generative AI for dataset annotation. Specifically, the GPT-4o-mini model was used to assign risk labels (Low, Medium or High Risk) to individual foot images, following a structured designed prompt for this task. The labeled dataset was used to train and evaluate two pre-trained CNN architectures, namely, ResNet50 and VGG16. Output predictions are obtained by aggregating the prediction for each of the images of a patient. Results: The results obtained show that both ResNet50 and VGG16 achieved good overall performance, with ResNet50 showing superior results. The High Risk class achieved the highest performance. The Low and Medium Risk classes also showed good performance but were prone to confusion due to the similar features of the images belonging to those classes. Conclusions: The technical contribution of the paper is a Streamlit App, available online for public use, showcases the work. The primary scientific contribution is the demonstration of how Generative AI can be used to train common CNN and automate a highly relevant healthcare process.