人工智能用于阻塞性睡眠呼吸暂停检测的诊断准确性:系统性审查
Sara Haghighat1,2, Muhammed Joghatayi3, Julien Issa2,4
1Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark.
BMC medical informatics and decision making
|July 29, 2025
概括
人工智能 (AI) 模型对诊断阻塞性睡眠呼吸暂停 (OSA) 有前途,为多睡眠学提供了潜在的替代方案. 深度学习和混合人工智能模型在检测OSA方面表现出高准确性,尽管需要进一步的研究才能广泛用于临床.
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:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种普遍存在的疾病,具有严重的健康影响.
- 目前的诊断方法,如多睡眠学 (PSG) 是资源密集型和难以获得的.
- 人工智能为更容易获得的OSA诊断提供了一个潜在的解决方案.
研究的目的:
- 系统地审查人工智能模型的诊断准确性,以检测阻塞性睡眠呼吸暂停.
- 评估各种AI架构在OSA诊断中的性能.
主要方法:
- 在六个主要数据库 (PubMed,Cochrane,Web of Science,Scopus,Embase,IEEE Xplore) 进行了系统的搜索.
- 研究包括成年人群,使用PSG作为参考,并使用在临床特征上训练的AI模型.
- 使用QUADAS-2检查清单进行了偏差风险评估.
主要成果:
- 13项研究符合纳入标准,利用深度学习,机器学习和混合人工智能模型.
- 报告的诊断准确率在67.03%至98.6%之间,深度学习和混合模型表现出卓越的性能.
- 七项研究显示偏差风险低,表明可靠性高.
结论:
- 人工智能模型,特别是深度学习和混合类型,显示出OSA诊断的巨大潜力.
- 关于人工智能模型的透明度,可解释性和可概括性的挑战仍然存在.
- 多样化的培训数据集对于提高AI性能和在OSA诊断中进行临床采用至关重要.
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