使用深度学习模型对心电图和胸部X射线应用的缺血性心脏病患者的多模式风险评估
Shinnosuke Sawano1, Satoshi Kodera1, Masataka Sato1
1Department of Cardiovascular Medicine, The University of Tokyo Hospital.
International heart journal
|January 31, 2024
概括
集成心电图 (ECG) 和胸部X射线 (CXR) 的深度神经网络 (DNN) 改善了缺血性心脏病 (IHD) 患者的风险评估. 使用DNN的多模式分析可以识别患主要心血管不良事件 (MACE) 的高风险个体.
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:
- 缺血性心脏病 (IHD) 的综合管理对于预后和治疗计划至关重要.
- 单模深度神经网络 (DNN) 在心脏异常检测方面表现有前途.
- 在IHD患者的多模式风险评估中,DNN的实用性仍未得到充分研究.
研究的目的:
- 评估多模式DNN方法在IHD患者风险分层方面的有效性.
- 预测主要不良心血管事件 (MACE) 使用集成的12导电图和胸部X射线 (CXR) 数据.
- 为了比较双模态与单模态DNN风险评估的预测能力.
主要方法:
- 开发了DNN模型,以从心电图中检测出左心室缩功能障碍 (LVSD) 和从CXR中检测出心壮病.
- 接受皮肤冠状动脉干预的2107名IHD患者被分为四个风险组:双模式高风险,心电图高风险,CXR高风险和无风险.
- 使用多变量Cox危险分析来评估MACE预测.
主要成果:
- 双模式高风险组表现出MACE的最高发病率 (P < 0.001).
- 多变量分析证实,与无风险组相比,双模式高风险组的MACE风险明显更高 (HR:2.370,P<0.001).
- 双模高风险组也显示MACE风险增加,而不是ECG高风险 (HR: 1.906,P = 0.010) 和CXR高风险 (HR: 1.624,P = 0.018) 组.
结论:
- 使用DNN整合心电图和CXR数据的多模式风险评估对IHD患者有效.
- 这种方法提高了对MACE高风险患者的识别.
- 这些发现支持通过DNN进行结合ECG和CXR分析的临床实用性,用于IHD管理.
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