Protocol for Metadata and Image Collection at Diabetic Foot Ulcer Clinics: Enabling Research in Wound Analytics and Deep Learning

Abstract

Background: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form and a comprehensive dataset named Zivot. Results: Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3,700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics. Conclusions: This work and the Zivot database offer a foundation for further exploration of holistic and multi-modal approaches to DFU research.

Special collection in association with the 2023 International Conference on aging, innovation and rehabilitation

“…While covering diverse topics, articles in this collection are linked through multiple connecting themes, such as functional electrical stimulation [1,2] or the application of signal processing and artificial intelligence in solving aging and rehabilitation problems [2][3][4][5][6][7][8]. Specifically, a number of the papers in this collection [4][5][6][7] explore the application of computer vision techniques in various healthcare domains, particularly focusing on rehabilitation and mobility assistance. Lim et al [2], for instance, investigate the feasibility of using depth cameras and pressure mats in a balance training system for individuals with spinal cord injuries.…”