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Zhe Huang¹, Weishen Pan¹, Shudhanshu Alishetti²

¹Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

NPJ digital medicine

|March 3, 2026

概括

一个新的AI框架使用心声图和健康记录预测心力衰竭 (HF) 患者的结果,改进了目前识别需要先进疗法的方法.

科学领域:

心脏病学心脏病学
人工智能的人工智能
医疗成像医学成像

背景情况:

心力衰竭 (HF) 影响着数百万人,患病率不断上升,预测工具有限.
心肺运动检测 (CPET) 对于HF预后至关重要,但面临实际限制.
准确预测功能能力对于指导HF治疗至关重要.

研究的目的:

开发一种多式人工智能框架,从胸前心声学 (TTE) 和电子健康记录 (EHR) 中预测峰值氧气消耗 (峰值VO2).
为了提高生存结果的预测,并识别高风险的HF患者.
在临床实践中克服传统CPET的局限性.

主要方法:

设计了一种多模式的多实例学习框架,以整合TTE和EHR数据.
该模型特别解决了跨模式相互作用和TTE研究的多实例性质.
基于R2进行高峰VO2预测和AUROC进行高风险患者鉴定.

主要成果:

人工智能框架实现了0.603的R2峰值VO2预测,超过了以前的方法 (R2=0.529).
它在识别高风险患者方面表现出强的表现,AUROC为0.849.
与之前的工作相比,外部验证显示R2 = 0.541和AUROC = 0.870的优异结果.

结论:

开发的AI框架为评估HF患者的功能能力和预后提供了更准确和更容易使用的方法.
这种方法可以改善对符合先进心力衰竭治疗条件的患者的识别.
该研究强调了使用先进的人工智能集成TTE和EHR数据的潜力,以更好地管理HF.

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