人工智能用于检测预后显著的左心室功能障碍从心声图
David Playford1, Simon Stewart2, Andrew Watts3
1Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia.
JACC. Advances
|June 24, 2025
概括
一个人工智能算法使用心声学准确地识别左心室 (LV) 功能障碍. 这种人工智能工具可靠地预测死亡风险,即使保留了射出分数 (EF) 和缺失的数据.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 心声图对于评估左心室 (LV) 功能至关重要.
- 识别 LV 功能障碍可能具有挑战性,特别是在保存的喷射分数 (EF) 时.
- 当前的方法可能会与不完整或模糊的心声回声数据作斗争.
研究的目的:
- 为了评估人工智能LV功能障碍 (AI-LVD) 识别算法的性能.
- 为了确定算法的能力来识别LV功能障碍从例行心声谱测量.
- 评估AI-LVD的预后价值,特别是在保存EF的情况下.
主要方法:
- 在大型心声回声数据集 (归算和训练队列) 上接受AI-LVD培训.
- 在81,509名不同EF类别患者的单独队列中验证了AI-LVD.
- 分析了AI-LVD与全因死亡率的相关性及其在缺少数据的情况下的表现.
主要成果:
- AI-LVD显示了与LV功能障碍严重程度和死亡风险相关的性别特异性输出.
- 该算法显示,在EF保存的患者中,该算法具有显著的预后能力.
- 更高的AI-LVD分值与男性和女性5年全因死亡率大幅增加有关.
结论:
- 一个新的AI-LVD算法有效地识别了仅使用心声回声数据的预后显著的LV功能障碍.
- 即使缺少关键心声学参数或保留EF,AI-LVD也可靠.
- 这种人工智能工具可以集成到常规心声回声学中,用于实时报告和风险分层.
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