透明的人工智能启用可解释和互动的睡眠呼吸暂停评估跨灵活的监测场景
Shuaicong Hu1, Jian Liu1, Yanan Wang1
1Department of Biomedical Engineering, College of Biomedical Engineering, Fudan University, Shanghai, China.
Nature communications
|August 14, 2025
概括
一个新的透明的人工智能系统,Apnea Interact Xplainer,使得早期的睡眠呼吸暂停检测和严重程度的分类. 它使用灵活的道分析来进行可访问,具有成本效益的诊断,改善患者的治疗结果.
科学领域:
- 医疗保健中的人工智能
- 睡眠医学 睡眠医学
- 呼吸系统生理学 呼吸系统生理学
背景情况:
- 未被诊断的睡眠呼吸暂停会导致严重的健康问题.
- 目前的大规模诊断受到无法访问的监测和对自动化分析缺乏信任的阻碍.
- 需要透明的AI框架来进行适应性监测.
研究的目的:
- 开发一个透明的AI系统,Apnea Interact Xplainer,用于睡眠呼吸暂停的诊断.
- 为了为临床和家庭环境提供灵活的道分析.
- 为了提高睡眠呼吸暂停诊断的早期检测和成本效益.
主要方法:
- 开发了一种透明的人工智能系统Apnea Interact Xplainer.
- 分析了来自七个多民族队伍的15807个多睡眠记录.
- 利用灵活的道分析和专家逻辑可解释的可视化.
主要成果:
- 实现了0.738-0.810准确度,用于四个级别的睡眠呼吸暂停严重程度分类.
- 在一个严重程度范围内达到99.8%的准确性,R平方为0.92-0.96的呼吸暂停-呼吸暂停指数预测.
- 仅使用氧计信号,对早期睡眠呼吸暂停检测有0.970的敏感性.
结论:
- 呼吸暂停交互Xplainer在睡眠暂停诊断中提供了一个范式转变.
- 该系统通过可解释的可视化实现透明,协作决策.
- 它促进了早期,经济有效的睡眠呼吸暂停检测和风险评估.
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