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人工智能面部识别阻塞性睡眠呼吸暂停:贝叶斯的元分析

Esther Yanxin Gao^1,2,3,4, Benjamin Kye Jyn Tan^5,6,7, Nicole Kye Wen Tan³

¹Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.

Sleep & breathing = Schlaf & Atmung

|November 30, 2024

概括

使用面照片的人工智能 (AI) 在诊断阻塞性睡眠呼吸暂停 (OSA) 方面表现出高准确度. 这种人工智能方法提供了一个具有成本效益的查工具,有可能改善初级保健机构的可访问性.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
睡眠医学睡眠医学

背景情况:

传统的诊断阻塞性睡眠呼吸暂停 (OSA) 通过多睡眠学是昂贵的,并不是广泛的.
新兴的人工智能 (AI) 技术为使用现有数据进行OSA诊断提供了新的方法.
面照片是人工智能驱动的OSA查的潜在低成本数据来源.

研究的目的:

为了评估人工智能算法的诊断准确性,AI算法训练了对阻塞性睡眠呼吸暂停 (OSA) 的面照片.
将基于AI的OSA诊断与传统诊断标准的性能进行比较.
从摄影数据中识别最有效的AI方法来检测OSA.

主要方法:

在主要的科学数据库 (PubMed,Embase,Scopus,Web of Science,IEEE Xplore) 进行了系统的文献搜索.
包括成人观察性研究,比较AI诊断性能使用面照片与呼吸暂停-呼吸暂停指数 (AHI) 标准.
对符合条件的研究进行了贝叶斯双变量元分析,并进行了偏差风险评估.

主要成果:

包括包括10个AI模型的6项研究,表明偏差风险低.
使用面照片的AI模型实现了84.9%的综合灵敏度和71.2%的OSA诊断的特异性.
深度学习模型,特别是卷积神经网络,表现出最高的准确性 (91.1%的灵敏度,79.2%的特异性),与家庭睡眠呼吸暂停测试相美.

结论:

在面照片上训练的AI显示了阻塞性睡眠呼吸暂停 (OSA) 的高诊断准确性.
这种人工智能驱动的方法是OSA的一个有希望的低成本选工具,提高了可访问性.
未来的研究将重点放在智能手机图像的深度学习上,可以进一步提高初级保健的可行性.

关键词:

深度学习是一种深度学习.诊断测试的准确性诊断测试的准确性机器学习机器学习神经网络的神经网络的神经网络睡眠障碍呼吸障碍呼吸障碍打打打打

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