一种基于人工智能的非侵入性方法,用于阻断性睡眠呼吸暂停患者的心血管疾病风险分层:叙述性审查
Luca Saba1, Mahesh Maindarkar2, Narendra N Khanna3
1Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy.
Reviews in cardiovascular medicine
|January 1, 2025
概括
阻塞性睡眠呼吸暂停 (OSA) 与心血管疾病 (CVD) 有关. 深度学习模型在检测OSA和分层CVD风险时显示出有前途,使用心血管成像,改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是心血管并发症的重要危险因素,包括心力衰竭.
- OSA和动脉样硬化心血管疾病 (ASCVD) 之间的关系使风险预测变得复杂.
- 目前的人工智能 (AI) 模型缺乏ASCVD和OSA患者中风风险的详细,无偏差分层.
研究的目的:
- 为了调查OSA和ASCVD/中风之间的关系.
- 为了评估深度学习 (DL) 风险分层使用心血管成像在OSA患者.
- 评估是否结合OSA风险可以改善心血管风险预测.
主要方法:
- 使用PRISMA指南进行系统审查,分析了191项关于OSA和动脉样硬化血管疾病的研究.
- 通过 carotid 超声波生物标志物探索 OSA 检测和 CVD 风险分层的 DL.
- 与现有研究对比DL策略.
主要成果:
- 确认了OSA和心血管疾病 (CVD) 之间的联系.
- DL模型有效地检测了OSA和分层CVD风险,使用带超声波.
- 在OSA患者中,DL对心血管疾病风险分层有好处,突出了AI的诸如偏见和可解释性等属性.
结论:
- 深度学习为OSA患者的心血管疾病风险分层提供了一种强大的方法.
- 关于开发无偏见,可解释的AI来预测ASCVD和OSA中风风险的建议.
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