一种深度学习支持的心电图和胸部X射线,用于检测肺动脉高血压
Pang-Yen Liu1,2, Shi-Chue Hsing1, Dung-Jang Tsai3,4
1Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan R.O.C.
Journal of imaging informatics in medicine
|August 13, 2024
概括
使用心电图 (ECG) 和胸部X射线 (CXR) 的人工智能 (AI) 模型可以检测升高的肺动脉压 (ePAP). 这种人工智能方法还可以预测未来的左心室功能障碍和心血管死亡风险.
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:
- 肺高血压的诊断和治疗随着新的标准和药物而发展.
- 人工智能 (AI) 在使用胸部X射线 (CXR) 和心电图 (ECG) 检测升高的肺动脉压 (ePAP) 方面表现有前途.
- 现有的人工智能模型在识别ePAP和预测心力衰竭住院或心血管死亡率方面表现出能力.
研究的目的:
- 开发和评估一个深度学习模型 (DLM),集成ECG和CXR用于ePAP检测.
- 评估DLM在预测未来左心室功能障碍 (LVD) 和心血管死亡率方面的表现.
- 在外部社区医院环境中验证集成AI模型.
主要方法:
- 开发了一个深度学习模型 (DLM),使用对联的心电图和CXR数据来检测ePAP (静心肺动脉压力>50 mmHg).
- 该DLM被内部和外部验证.
- 评估了该模型预测新发性LVD和心血管死亡率的能力.
主要成果:
- 结合的心电图和CXR模型在ePAP检测方面获得了高AUC (0.8644内部,0.8734外部).
- 该模型显示了ePAP检测的强烈负预测值 (NPV) (98%内部,98.1%外部).
- 综合人工智能模型预测了内部数据集中LVD (HR 4.51) 和心血管死亡率 (HR 6.08) 的风险增加,在外部验证中也有类似的发现.
结论:
- 集ECG和CXR的DLM有效地检测ePAP,具有高精度和NPV.
- 该模型准确地预测了LVD和心血管死亡率的未来风险.
- 这种人工智能方法有可能加快早期肺高血压的识别,指导进一步的诊断评估并改善心血管结果.
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