基于自动面部识别和机器学习的阻塞性睡眠呼吸暂停的自助检测
Qi Chen1, Zhe Liang2, Qing Wang3,4
1Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Sleep & breathing = Schlaf & Atmung
|June 6, 2023
概括
人工智能和面部照片可以帮助选阻塞性睡眠呼吸暂停 (OSA). 这种使用面部特征和临床数据的人工智能模型,显示了快速,非侵入性的OSA查的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 的诊断是复杂的,并且经常延迟.
- 目前的诊断方法是耗时的,并不是普遍可用的.
研究的目的:
- 通过使用面部照片和临床数据,评估AI驱动的OSA查工具的有效性.
- 开发一种快速,非侵入性的OSA查方法.
主要方法:
- 收集了来自653名怀疑患有OSA的受试者的2D面部照片和临床数据.
- 使用自动化面部特征识别和机器学习 (CATBOOST算法).
- 使用睡眠监测作为黄金标准验证了该模型,采用十倍交叉验证.
主要成果:
- CATBOOST模型实现了0.76的AUC,超过了传统查问卷.
- 关键预测因素包括目击呼吸暂停,BMI,子周长,面部参数和高血压.
- 该模型显示,对于经常躺卧睡眠呼吸暂停的患者,灵敏度更高 (0.94).
结论:
- 从二维照片中的面特征,特别是下部分,可以预测中国人口中的OSA.
- 基于人工智能的面部识别提供了一个快速,无辐射和可重复的OSA自我查方法.
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