AI-Powered Remote Monitoring for Lower Extremity Wound Management: A Randomized Controlled Trial Protocol.

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
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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.

人工智能远程监测下肢伤口管理:一项随机对照试验方案。
背景:与糖尿病相关的下肢伤口是一个严重的全球健康问题,糖尿病足溃疡影响了12%至25%的成人糖尿病患者,占美国所有下肢截肢的80-90%。下肢伤口的全面亲自护理很重要,但对患者来说可能是负担,对卫生保健系统来说也是昂贵的。一种具有成本效益的远程医疗模型,使用集成智能手机的数字应用程序,通过机器学习算法实时远程分析伤口状态,可以使患者更容易获得下肢伤口护理。该试验旨在确定与标准的亲自就诊相比,人工智能(AI)驱动的数字远程监测是否是一种可行的、以患者为中心的远程伤口监测和管理解决方案。方法:我们将在马里兰州巴尔的摩市约翰霍普金斯医院多学科糖尿病足和伤口诊所(ClinicalTrials.gov: NCT05579743)对120例下肢活动性伤口患者进行非盲随机对照试验。参与者将被随机分配为1:1,接受使用人工智能驱动的远程伤口监测技术的伤口护理监测。)或标准的现场监测,为期12周。主要目的是建立一种以患者为中心的新型远程监测方案的可行性,用于监测和监测下肢伤口。次要目标包括与标准的现场监测相比,评估患者和提供者对远程伤口监测技术的满意度;并生成关于伤口愈合时间和主要截肢率的试点数据,这些数据是由远程监控的患者与接受标准治疗的患者进行比较的。结论:本试验将确定人工智能驱动的远程数字监测是否可行和可接受,作为标准现场监测的替代方案,用于监测和管理活动性下肢伤口患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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