Y H Andrew Wu, Alana C Keegan, Midori P Starks White, Sanuja Bose, Sherry G Leung, Ronald Sherman, Christopher J Abularrage, Elizabeth Selvin, Caitlin W Hicks
{"title":"AI-Powered Remote Monitoring for Lower Extremity Wound Management: A Randomized Controlled Trial Protocol.","authors":"Y H Andrew Wu, Alana C Keegan, Midori P Starks White, Sanuja Bose, Sherry G Leung, Ronald Sherman, Christopher J Abularrage, Elizabeth Selvin, Caitlin W Hicks","doi":"10.1016/j.jvsvi.2025.100279","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Lower extremity wounds associated with diabetes are a serious global health issue, with diabetic foot ulcers affecting 12% to 25% of adults with diabetes and accounting for 80-90% of all lower extremity amputations in the United States. Comprehensive in-person care for lower extremity wounds is important but can be burdensome for patients and costly for healthcare systems. A cost-effective telehealth model using a smartphone-integrated digital application that remotely analyzes wound status with machine-learning algorithms in real-time could make lower extremity wounds care more accessible to patients. This trial aims to determine if an artificial intelligence (AI)-powered digital remote monitoring is a feasible, patient-centered solution for remote wound monitoring and management compared to standard in-person visits.</p><p><strong>Methods: </strong>We will conduct a non-blinded randomized control trial of 120 patients with active lower extremity wounds treated in the Johns Hopkins Hospital Multidisciplinary Diabetic Foot and Wound Clinic in Baltimore, Maryland (ClinicalTrials.gov: NCT05579743). Participants will be randomly assigned 1:1 to receive wound care monitoring using AI-powered remote wound monitoring technology (Healthy.io Ltd.) or standard in-person monitoring for 12 weeks. The primary aim is to establish the feasibility of a novel remote patient-centered monitoring program for the surveillance and monitoring of lower extremity wounds. Secondary aims include evaluating patient and provider satisfaction with remote wound monitoring technology compared to standard in-person monitoring; and generating pilot data on wound healing time and major amputation rates in patients who are monitored remotely compared to patients treated with standard of care.</p><p><strong>Conclusion: </strong>This trial will determine whether AI-powered remote digital monitoring is feasible and acceptable as an alternative to standard in-person monitoring for the monitoring and management of patients with active lower extremity wounds.</p>","PeriodicalId":74034,"journal":{"name":"JVS-vascular insights","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377042/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JVS-vascular insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jvsvi.2025.100279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Lower extremity wounds associated with diabetes are a serious global health issue, with diabetic foot ulcers affecting 12% to 25% of adults with diabetes and accounting for 80-90% of all lower extremity amputations in the United States. Comprehensive in-person care for lower extremity wounds is important but can be burdensome for patients and costly for healthcare systems. A cost-effective telehealth model using a smartphone-integrated digital application that remotely analyzes wound status with machine-learning algorithms in real-time could make lower extremity wounds care more accessible to patients. This trial aims to determine if an artificial intelligence (AI)-powered digital remote monitoring is a feasible, patient-centered solution for remote wound monitoring and management compared to standard in-person visits.
Methods: We will conduct a non-blinded randomized control trial of 120 patients with active lower extremity wounds treated in the Johns Hopkins Hospital Multidisciplinary Diabetic Foot and Wound Clinic in Baltimore, Maryland (ClinicalTrials.gov: NCT05579743). Participants will be randomly assigned 1:1 to receive wound care monitoring using AI-powered remote wound monitoring technology (Healthy.io Ltd.) or standard in-person monitoring for 12 weeks. The primary aim is to establish the feasibility of a novel remote patient-centered monitoring program for the surveillance and monitoring of lower extremity wounds. Secondary aims include evaluating patient and provider satisfaction with remote wound monitoring technology compared to standard in-person monitoring; and generating pilot data on wound healing time and major amputation rates in patients who are monitored remotely compared to patients treated with standard of care.
Conclusion: This trial will determine whether AI-powered remote digital monitoring is feasible and acceptable as an alternative to standard in-person monitoring for the monitoring and management of patients with active lower extremity wounds.