用临床和CMR衍生的特征在心力衰竭中使用保存的喷射分数进行多模式风险分层:一种包含模型可解释性的方法
Shiwen Zhang1, Yuan Lin2, Dongqi Han2
1Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China (S.Z.).
Academic radiology
|July 18, 2025
概括
这项研究表明,将临床数据与心脏MRI衍生后心脏脂肪组织 (EAT) 体积相结合,可以使用可解释的AI模型来改善心脏衰竭与保存的喷射分数 (HFpEF) 诊断和结果预测.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 由于其异质性,心脏衰竭与保留喷射分数 (HFpEF) 存在诊断和预后挑战.
- 可通过心磁共振 (CMR) 测量的心上脂肪组织 (EAT) 体积与HFpEF病理生理学有关.
研究的目的:
- 开发和验证一个多模式,可解释的机器学习框架,以改善HFpEF中的风险分层和结果预测.
- 将临床变量和CMR衍生特征,特别是EAT体积整合到预测模型中.
主要方法:
- 追溯分析301名参与者 (171名HFpEF,130名对照).
- 在机器学习模型中整合临床数据,CMR衍生 EAT 量和生物标志物.
- 使用准确度,精度,回忆,F1分数,ROC-AUC和PR-AUC评估模型性能.
主要成果:
- HFpEF患者的EAT体积和NT-proBNP水平显著增加.
- 一个投票分类器实现了高精度 (0.94) 的HFpEF诊断.
- 整体模型 (AdaBoost,XGBoost,随机森林) 准确预测了不良结果 (准确率:0.95),以 EAT 量,右AVG,TRV 和代谢综合征作为关键预测因素.
结论:
- 结合临床和CMR衍生特征的可解释模型,特别是EAT体积,可以提高HFpEF诊断和预后.
- 一种数据驱动的,可解释的方法有助于表征HFpEF表型和个性化风险评估.
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