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人工智能远程监控下肢伤口管理:随机控制试验协议

Y H Andrew Wu^1,2,3, Alana C Keegan^1,4, Midori P Starks White¹

¹Division of Vascular Surgery and Endovascular Therapy, Johns Hopkins University, Baltimore, MD.

JVS-vascular insights

|August 26, 2025

概括

这项研究探讨了人工智能驱动的糖尿病下肢伤口远程监测,旨在改善与传统面对面访问相比的可访问性和患者护理.

科学领域:

在伤口护理领域的数字健康创新.
人工智能在医学中的应用.
远程医疗和远程监测患者

背景情况:

糖尿病下肢伤害是全球严重的健康问题,导致截肢率高.
目前对糖尿病伤口的个人护理至关重要,但对患者和医疗保健系统构成挑战.
一个具有成本效益的远程医疗模型与人工智能分析可以提高糖尿病伤口管理的可访问性.

研究的目的:

确定人工智能驱动的数字远程监测下肢伤害的可行性和患者中心性.
评估远程伤口监测作为标准人身护理的替代方案的可接受性.
通过人工智能监测收集伤口愈合和截肢率的试点数据.

主要方法:

一项非盲目的随机对照试验,涉及120名下肢受伤患者.
参与者随机分配到人工智能驱动的远程监控或标准面对面监控12周.
使用智能手机集成的数字应用程序与机器学习算法进行实时伤口分析.

主要成果:

该研究旨在确定人工智能驱动的远程监控计划的可行性.
其次目标包括评估患者和提供者对该技术的满意度.
创伤愈合时间和重大截肢率的试点数据将用于比较.

结论:

人工智能远程数字监控显示为一个可行的,可接受的替代方案来管理下肢伤口.
这种方法可以提高糖尿病伤病患者护理的可及性和效率.
需要进一步评估以确认对治疗时间和截肢率等临床结果的影响.

关键词:

人工智能下肢受伤机器学习随机对照试验远程监控远程伤口监测技术

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