肺高血压的人工智能:系统性审查
Tilmann Kramer1,2, Mira Kramer3, Christian Hagist4
1Department of Internal Medicine III, Heart Center at the University of Cologne, Cologne, Germany. tilmann.kramer@uk-koeln.de.
人工智能 (AI),包括机器学习 (ML) 和深度学习 (DL),显示出预测和诊断肺高血压 (PH) 和肺动脉高血压 (PAH) 的前景. 需要进一步的研究,以解决临床使用的模型验证和透明度问题.
科学领域:
- 医疗信息学 医疗信息学
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
背景情况:
- 肺高血压 (PH) 涉及肺压力升高和右心室应变.
- 肺动脉高血压 (PAH) 的预后不好,特别是在诊断迟到的情况下.
- 人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 为非侵入性PH/PAH预测和风险分层提供了潜力.
研究的目的:
- 系统地审查ML和DL应用程序的非侵入性诊断,分类和预后在PH和PAH.
- 评估这些AI模型的方法质量和临床适用性.
- 使用SWiM框架综合研究特征和异质性.
主要方法:
- 在非侵入性临床,成像或生物标记数据上使用ML/DL进行PRISMA指导的系统搜索研究.
- 通过SWiM框架综合研究特征和异质性.
- 使用PROBAST+AI进行偏见风险评估,重点关注参与者选择,预测因素,结果和分析.
主要成果:
- 包括53项研究,主要使用临床,心声,成像或分子数据.
- 人工智能模型显示出高预测性能 (AUC 0.711.00),DL,特别是CNN,越来越多地应用.
- 大多数研究是回顾性,单一中心,有限的外部验证和设计和结果的异质性;83%的偏差风险低,但普遍存在适用性问题.
结论:
- ML和DL模型对PH和PAH的诊断和预后显示出显著的希望.
- 限制包括分类区分,方法透明度和需要强有力的外部验证.
- 未来的研究应该专注于解决这些局限性,以提高临床适用性.
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