通过多中心研究开发和验证人工智能增强的耳声检测用于膜心脏病查
Andrew McDonald1, Mark Gales1, Bushra S Rana2
1Department of Engineering, University of Cambridge, Cambridge, UK.
NPJ cardiovascular health
|February 13, 2026
概括
一个新的人工智能工具使用听力镜记录直接检测膜心脏病 (VHD),改善早期诊断. 这种先进的查方法对严重的大动脉狭窄和 mitrale 吐具有很高的准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心脏病 (VHD) 影响着越来越多的人口,超过一半的病例由于晚期症状和有限的查而未被诊断出来.
- 目前用于VHD查的AI工具依赖于声检测,对某些VHD亚型缺乏灵敏度,并且受到小数据集的限制.
研究的目的:
- 开发和验证一种新型的神经网络,从听力镜记录中直接预测临床意义上的VHD,并使用心声回声数据进行训练.
- 为了提高VHD查的敏感性,并使早期诊断相比现有方法.
主要方法:
- 汇编了来自英国初级保健和医院环境的1767名患者的多样化数据集,包括听力镜记录和心声回声标签.
- 一个反复的神经网络被训练使用心声回声的目标,而不是的标签,直接预测VHD.
- 算法的性能是使用接收器操作特征曲线 (AUROC) 下的面积来评估的.
主要成果:
- 经过训练的神经网络获得了0.83的AUROC,在VHD预测方面表现优于全科医生.
- 该算法对严重的大动脉狭窄 (98%) 和严重的 mitrale 吐 (94%) 显示出高灵敏度.
- 该研究利用了各种患者数据,并验证了人工智能工具在现实世界临床环境中的有效性.
结论:
- 这种新的AI算法显示出作为一个可扩展的,低成本的查工具的显著前景,用于膜心脏病.
- 通过这种人工智能驱动的听力镜分析,早期检测VHD可以促进及时的转诊和干预.
- 这种方法代表了非侵入性VHD查的重大进步,解决了当前方法的局限性.
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