一种基于机器学习的自动化定量多参数方法,用于对腹的重度分级
Anita Sadeghpour1, Zhubo Jiang2, Yoran M Hummel2
1MedStar Health Research Institute and Georgetown University, Washington, District of Columbia, USA.
JACC. Cardiovascular imaging
|August 17, 2024
概括
一个自动化的机器学习工具准确地从心声回声图 (echocardiograms) 中评分二手上 (mitral regurgitation,MR) 的严重程度. 该工具选显著的MR,改善患者护理和预测死亡率.
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:
- 副腹 (MR) 是常见的,但其严重程度的评估是主观和可变的.
- 一个自动化工具可以标准化MR分级并改善患者管理.
研究的目的:
- 开发和验证一个完全自动化的机器学习 (ML) 工作流程,以使用心声回声学对MR严重程度进行分级.
- 评估ML模型在分类MR严重性的准确性和效率.
主要方法:
- ML算法在两个队列的回声心电图上进行训练,并在两个独立研究中得到验证.
- 该模型测量了16个MR相关参数,最佳模型使用了9个参数.
- 基本真相是通过多参数心声回声核实验室评估来确定的.
主要成果:
- 自动化工作流程在识别显著 (中度或严重) 的MR时达到97%的准确性.
- 图像分析速度很快 (每例80±5秒),在99.3%的病例中可行.
- 根据模型的严重MR分级预测了更高的1年死亡率 (调整HR:5.20).
结论:
- 用于自动化MR严重程度分级的多参数ML模型是可行的,准确的和快速的.
- 这种工具可以简化诊断,促进及时转诊,并改善患者的治疗结果.
- 该模型预测死亡率的能力突显了其临床意义.
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