基于心声学的深度学习模型来区分约束性心周炎和限制性心肌病
Chieh-Ju Chao1, Jiwoong Jeong2, Reza Arsanjani3
1Mayo Clinic Rochester, Rochester, Minnesota, USA; Mayo Clinic Arizona, Scottsdale, Arizona, USA.
JACC. Cardiovascular imaging
|November 9, 2023
概括
一个使用心声学的人工智能模型有效地区分了收缩性心膜炎 (CP) 和心脏粉症. 这种人工智能工具有助于早期发现CP,改善患者的治疗结果和工作流程效率.
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:
- 狭窄性心膜炎 (CP) 是透心性心力衰竭的可逆原因,但诊断是具有挑战性的.
- 人工智能 (AI) 提供了改善CP识别的潜力.
研究的目的:
- 开发和评估一个深度学习模型,使用跨胸腔心声学来区分CP与心脏粉症 (CA).
主要方法:
- 一个ResNet50深度学习模型被训练在381名患者 (184名CP,197名CA) 的四腔心声图上.
- 模型的性能是通过在持有测试组上的曲线下的面积 (AUC) 来评估的.
- 使用GradCAM进行模型解释.
主要成果:
- 在区分CP与CA时,AI模型实现了0.97的AUC.
- 格拉德卡姆分析表明,模型的重点是心室隔膜区域.
结论:
- 使用标准心声回声图视图的AI模型可以促进CP检测.
- 这种方法可以提高工作流程的效率,并使患者能够迅速转诊进行干预.
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