用人工智能启用心电图用于慢性肝病风险预测:系统性审查
Christian Akem Dimala1, Yazan A Al-Ajlouni2, Nso Nso3
1Division of Cardiovascular Medicine, University of Texas Medical Branch, Galveston, TX, United States of America.
International journal of cardiology
|September 25, 2025
概括
人工智能启用心电图 (AI-ECG) 显示了慢性肝病 (CLD) 风险分层的前景. 虽然AI-ECG模型显示出潜力,但在检测肝硬化等疾病时,需要进一步精细化才能获得广泛的临床采用.
科学领域:
- 心脏病学 心脏病学
- 肝病学 肝病学是一种肝病学.
- 人工智能的人工智能
背景情况:
- 人工智能启用心电图 (AI-ECG) 使用机器学习来检测微妙的心电图异常,增强风险分层.
- 本系统性审查侧重于AI-ECG在慢性肝病 (CLD) 患者风险预测方面的性能和临床实用性.
研究的目的:
- 评估AI-ECG模型在预测CLD患者肝硬化,食道静脉和代谢功能障碍相关的脂肪性肝病 (MASLD) 中的有效性.
- 评估各种AI-ECG算法的肝病风险分层的诊断性能 (AUC,灵敏度,特异性).
主要方法:
- 在主要数据库 (PubMed,EMBASE,Cochrane,Scopus) 进行了系统的文献搜索,截至2024年11月28日.
- 包括分析AI-ECG模型用于预测CLD患者肝硬化,食道静脉和MASLD的研究.
- 数据提取和合成的重点是模型性能指标和与临床严重性得分的相关性.
主要成果:
- 分析了四项涉及133,408名参与者的研究.
- AI-ECG-肝硬化 (ACE) 模型在肝硬化检测方面取得了最高的性能 (AUC:0.908).
- 其他模型,包括基于CNN的算法和DULCE模型,表现不同,其中一些显示食道静脉和MASLD检测的AUC较低.
结论:
- AI-ECG提出了一种新的,非侵入性的方法,用于早期检测和CLD风险分层.
- 当前AI-ECG模型的敏感性和特异性需要在例行临床实施之前进行改进.
- 未来的研究应该优先考虑AI-ECG模型的优化,前性验证和标准化,以实现无临床集成.
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