扩张功能评估:机器学习改善了左心室填充压力的分类
Faraz H Khan1, Katsuji Inoue1,2, Nobuyuki Ohte3
1Institute for Surgical Research and Department of Cardiology, Oslo University Hospital, Rikshospitalet, and University of Oslo, Oslo, Norway.
与当前的指导方针相比,机器学习 (ML) 模型展示了左心室填充压力 (LVFP) 的改进分类,提供了更高的可行性,并确定了评估的新关键参数.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 目前的左心室填充压力 (LVFP) 分类依赖于专家定义的心声回声算法.
- 这些算法可以通过缺少数据和次优参数选择来限制.
研究的目的:
- 评估机器学习 (ML) 在改进LVFP分类方面的有效性.
- 确定ML模型对LVFP评估的重点回声心脏学参数.
主要方法:
- 一项多中心研究,涉及250名接受心声图和心脏导管治疗的患者.
- 通过嵌套交叉验证来训练和验证8个ML模型,以对LVFP进行分类.
- ML模型执行参数选择以优化分类性能.
主要成果:
- ML模型实现了82%-86%的分类精度,超过了2016年ASE/EACVI指南 (81%的精度,13%未分类).
- ML模型有效处理缺失的参数值,对所有患者进行分类.
- 通过ML识别的关键参数包括双肩E/左心房储应变,log{NT-proBNP}和三腹吐速度.
结论:
- ML显著提高了LVFP的分类,提高了可行性.
- 该研究通过ML驱动的洞察力突出了LVFP评估中较少使用的参数的价值.
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