基于人工智能的医疗支持患者监测系统
Eui-Sun Kim1, Sung-Jong Eun2, Khae-Hawn Kim1
1Department of Media, Soongsil University, Seoul, Korea.
International neurourology journal
|January 3, 2024
概括
一个新的人工智能驱动的监测系统准确地跟踪排尿活动并分析运动,以帮助预防神经性膀,为尿路功能障碍患者提供潜在的治疗益处.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 尿路功能障碍影响许多患者,需要先进的监测和管理解决方案.
- 目前用于管理排尿条件的方法可能缺乏自动追踪和预测能力.
- 神经性膀具有重大风险,需要采取积极的预防策略.
研究的目的:
- 开发基于人工智能 (AI) 的监测系统,用于管理和预防与排尿相关的疾病.
- 实施人工智能技术,自动记录排尿活动.
- 为预防神经性膀创建一种运动分析技术.
主要方法:
- 开发基于人工智能的识别技术,使用循环神经网络进行排尿活动.
- 采用基于卷积神经网络 (CNN) 的AI进行运动分析,以预测神经性膀风险.
- 在60天内对30名尿路功能障碍患者进行了系统性能评估.
主要成果:
- 排尿活动识别技术实现了94.2%的平均准确性.
- 基于CNN的AI运动分析技术用于神经性膀预防,平均准确率为83%.
- 该系统有效地处理了用于患者监控的图像和信号数据.
结论:
- 开发了一种新的AI驱动的监测系统,用于预测和管理尿道问题患者的风险.
- 该系统通过利用人工智能进行数据处理来支持患者护理周期.
- 预计将演变为数字治疗产品,以提高患者的益处.
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