从12导电心电图中基于人工智能识别左心室缩功能障碍:对现有模型的外部验证和高级应用
Sebastian König1,2, Sven Hohenstein2, Anne Nitsche2
1Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.
European heart journal. Digital health
|March 20, 2024
概括
这项研究对人工智能 (AI) 模型进行了外部验证,用于使用心电图 (ECG) 检测左心室缩功能障碍 (LVSD). 人工智能模型表现出强的性能,对心血管疾病诊断有潜在的影响.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 人工智能 (AI) 模型显示出从心电图 (ECG) 检测心血管疾病的前景.
- 外部验证对人工智能算法至关重要,但并非普遍可用.
- 左心室缩功能障碍 (LVSD) 是一种关键的心血管疾病,需要精确检测.
研究的目的:
- 为了外部验证先前开发的基于人工智能的算法,用于从12导电心电图中检测LVSD.
- 评估AI模型在不同患者子组和心电图特征中的性能.
- 调查人工智能检测到的LVSD与未来心脏事件之间的关联.
主要方法:
- 对42,291个配对的12导电心电图和心声回声图 (7天内) 的回顾性分析.
- 将先前存在的AI模型应用于ECG数据以计算LVSD概率.
- 计算接收器操作特征曲线 (AUROC) 下的面积,灵敏度和特异性;在≥3个月后使用心声回声仪进行后续分析.
主要成果:
- 人工智能模型在LVSD检测方面实现了0.88的整体AUROC.
- 最佳的截止值产生了82%的灵敏度和77%的特异性.
- 在高心率,心房动和广泛QRS复合体的子组中,AUROCs降低了.
- 高AI预测的LVSD概率与随访期间LVSD发展的风险增加四倍相关.
结论:
- 该研究为基于AI的ECG分析模型在检测LVSD时提供了强大的外部验证.
- 人工智能模型展示了强大的性能指标来识别LVSD.
- 需要进一步的前性研究来评估虚假阳性LVSD查和随后的腹腔功能变化的临床意义.
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