AUTHOR=Sendilraj Varun , Pilcher William , Choi Dahim , Bhasin Aarav , Bhadada Avika , Bhadadaa Sanjay Kumar , Bhasin Manoj 

TITLE=DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1386613

DOI=10.3389/fendo.2024.1386613

ISSN=1664-2392

ABSTRACT=Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients and often result in amputation and even mortality. Early recognition of infection and ischemia is crucial for improved healing, but current methods are invasive, time-consuming, and expensive. To address this need, we have developed DFUCare, a platform that uses computer vision and deep learning (DL) algorithms to non-invasively localize, classify, and analyze DFUs. The platform uses a combination of CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization achieving an F1-score of 0.80 and an mAP of 0.861. Using DL algorithms to identify infection and ischemia, we achieved a binary accuracy of 79.76% for infection classification and 94.81% for ischemic classification on a validation set. DFUCare also measures wound size and performs tissue color and textural analysis to allow comparative analysis of macroscopic features of the wound. We tested DFUCare performance in a clinical setting to analyze the DFUs collected using a cell phone camera. DFUCare successfully segmented the skin from the background, localized the wound with less than 10% error, and predicted infection and ischemia with less than 10% error. This innovative approach has the potential to deliver a paradigm shift in diabetic foot care by providing a cost-effective, remote, and convenient healthcare solution.