机器学习驱动的得分的推导和验证,以预测内心肌瘤活检的诊断产量
Christian Basile1,2,3, Christian L Polte4,5, Piero Gentile6,7
1Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
NPJ digital medicine
|February 9, 2026
概括
一个新的机器学习得分可以使用非侵入性数据预测诊断内肌心活检 (EMB) 的可能性. 这种工具有助于决定何时对心肌病进行EMB,从而提高诊断准确度.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 内心肌活检 (EMB) 是诊断心肌病的黄金标准,但诊断收益率很低.
- 需要使用非侵入性方法来更好地选择患者接受EMB.
研究的目的:
- 开发和验证基于机器学习的得分,以使用非侵入性数据预测诊断EMB的可能性.
- 改善在心力衰竭患者中执行EMB的决策过程.
主要方法:
- 对775名接受EMB治疗的心力衰竭患者的回顾性分析.
- 使用非侵入性预测因素开发一个随机森林模型.
- 在171名患者的独立队列中进行外部验证.
主要成果:
- 在19.9%的病例中,EMB产生了明确的诊断,其中粉样症是最常见的 (50%).
- 诊断EMB最强的非侵入性预测因素包括右心室晚期加多增强 (LGE),左心室和心房LGE,NTproBNP和功能.
- 机器学习得分显示出优异的区别 (交叉验证中的AUC为0.92,测试中的0.91和外部验证中的0.82).
结论:
- 使用非侵入性数据的基于机器学习的得分可以有效地预测诊断内心肌瘤活检的可能性.
- 这一分数提供了一个有价值的非侵入性工具,以帮助临床医生在心力衰竭患者的EMB决策过程中.
- 开发的得分显示了不同患者队列的一致表现,支持其潜在的临床实用性.
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