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ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Translational and Clinical Endocrinology
Volume 15 - 2024 |
doi: 10.3389/fendo.2024.1386613
DFUCare: Automated Robust Diabetic Foot Ulcer Monitoring Platform 1 DFUCare: Deep learning platform for diabetic foot ulcer detection, analysis, and monitoring
Provisionally accepted- 1 Wallace H. Coulter Department of Biomedical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
- 2 Johns Creek High School, Johns Creek, United States
- 3 vivek high school, chandigarh, India
- 4 Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, Haryana, India
- 5 Emory University, Atlanta, United States
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.
Keywords: diabetic foot ulcer, Machine Learing, deep learning - artificial intelligence, Wound monitoring, Remote health care monitoring
Received: 15 Feb 2024; Accepted: 02 Sep 2024.
Copyright: © 2024 Sendilraj, choi, pilcher, Bhasin, bhadada, Bhadada and Bhasin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Manoj Bhasin, Emory University, Atlanta, United States
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