使用心电图,胸部X射线和大脑 natriuretic来检测肺高血压的一组学习模型
Risa Kishikawa1, Satoshi Kodera1, Naoto Setoguchi1
1Department of Cardiovascular Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
European heart journal. Digital health
|March 20, 2025
概括
整合了ECG,CXR和BNP数据的AI模型显著提高了心脏病学家诊断肺高血压 (PH) 的准确性. 这一进步有望更早地检测PH,并为PH提供更好的患者结果.
科学领域:
- 医疗人工智能 医疗人工智能
- 心脏病学 心脏病学
- 诊断成像 诊断成像 诊断成像
背景情况:
- 肺高血压 (PH) 的延迟诊断是导致患者预后不佳的重要因素.
- 准确及时检测PH对于有效的患者管理和改善结果至关重要.
研究的目的:
- 开发和评估人工智能 (AI) 组合学习模型,用于早期检测肺高血压 (PH).
- 整合来自心电图 (ECG),胸部X射线 (CXR) 和大脑天然尿素 (BNP) 水平的数据,以加强PH诊断.
主要方法:
- 为ECG (卷积神经网络),BNP (逻辑回归) 和CXR (ResNet18深度学习) 开发了个别的AI模型.
- 将这些模型集成到一个三层完全连接的多式联络组合学习模型中.
- 通过比较解释测试,评估模型的性能及其对心脏病学家诊断准确性的影响.
主要成果:
- 集体学习模型实现了接收器操作特征曲线下的面积为0.872 (95% CI,0.829-0.915).
- 心脏病学家的诊断准确度从没有人工智能模型的65.0 ± 4.7%提高到使用人工智能模型的74.0 ± 2.7% (P < 0.01).
- 单个模型的性能:心电图 (0.818),CXR (0.823) 和BNP (0.724).
结论:
- 开发的集体学习人工智能模型显著提高了心脏病学家使用多式数据 (ECG,CXR,BNP) 检测PH的准确性.
- 这种人工智能驱动的方法有助于更早,更精确的PH诊断,从而有可能改善患者的预后.
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