TDNet Discover

Background: Diabetic foot ulcer (DFU) complications continue to represent a major health obstacle, not only in cost but in quality of life for diabetic patients with or without polyneuropathy, despite current research and updates to standard care. Continuous DFU monitoring has been at the forefront of recent research to aid in bridging the gaps between inperson clinic visits and providing real-time data to providers of at-risk patients who may need earlier clinic follow-ups. This has the potential to prevent the occurrence of diabetic foot ulcers by alerting providers of early signs of inflammatory skin changes and allowing for prompt evaluation and management to reduce or mitigate wound development. Purpose: This research sought to evaluate and determine the use of AI-based technology in the management of DFU and its potential role in decreasing associated health burdens. Methods: Three researchers searched 3 databases using predetermined key terms, inclusion, and exclusion criteria. Because this research is relatively new, there were not many studies regarding this subject. An independent faculty researcher was involved and found 2 additional articles appropriate for this EBCR. All 3 articles were reviewed for quality by 2 of 3 researchers and if approved by consensus, a subsequent data extraction was performed and reviewed by another researcher. The primary outcomes assessed were plantar 2-point temperature differences, rate of wound development, amputation rate, ulcer severity, number of visits paid to outpatient podiatry clinic, hospitalization rates, device accuracy, and participant disposition and adherence. Results: This research found statistically significantly lower rates in development of DFU when monitoring plantar temperature differences between 2 points (95% CI 0.859-1.20, p <.001); Cohen’s d = 0.79; 95% CI 0.76–0.81; P = 0.0), rate of wound development ((RRR) 0.68; 95% CI 0.52-0.79; (NNT) 5.0; P<0.001), amputation rate (RRR 0.83; 95% CI 0.39-0.95; NNT 41.7; v P<0.006), severity of foot ulcers (RRR 0.86; 95% CI 0.70-0.93; NNT 15.3; P<0.001). Moreover, it found a decrease in the number of visits paid to outpatient podiatry clinic (RRR 0.31; 95% CI 0.24-0.37; NNT 0.46; P<0.001) and hospitalization rates (RRR 0.63; 95% CI 0.33-0.80; NNT 5.7; P<0.002) with the addition of AI based at home monitoring devices to the patient current care plan. Conclusions: The results of this study suggest that AI technology could play a vital role in the prevention, management, and treatment of diabetic foot ulcers. Implementing AI technology as an adjunct to current standard of care practices could provide significant benefit through continuous monitoring, earlier detection, and proactive prevention of DFUs prior to their development. This could help reduce health the burden and financial cost, as well as improve the patients’ quality of life.