在使用人工智能启用心电图的儿科患者中检测右和左心室功能障碍
Scott Anjewierden1, Donnchadh O'Sullivan1, Kathryn E Mangold2
1Department of Pediatrics and Adolescent Medicine Mayo Clinic Rochester MN USA.
Journal of the American Heart Association
|November 4, 2024
概括
人工智能现在可以使用心电图在儿童中检测左心室和右心室缩功能障碍. 针对儿童的AI模型显示,在年轻患者中早期检测这些心脏病的准确性有所提高.
科学领域:
- 儿科心脏病学 儿科心脏病学
- 医学中的人工智能.
- 电心电图是指心电图.
背景情况:
- 在儿童中早期发现左和右心室缩功能障碍 (LVSD和RVSD) 对于降低发病率和死亡率至关重要.
- 现有的用于LVSD/RVSD检测的AI算法已在成年人中得到验证,但它们在儿科群体中的有效性尚不确定.
研究的目的:
- 开发新型的人工智能支持的心电图算法,用于检测儿科患者的LVSD和RVSD.
- 评估这些儿科特异型模型的性能与成人衍生型号相比.
主要方法:
- 使用了10,142名儿科患者 (年龄≤18) 的数据集,使用了12导电心电图和胸前心脏回声图.
- 开发了新的AI模型来检测LVSD (由LVEF值定义) 和RVSD.
- 将儿科模型的性能指标 (AUC,灵敏度,特异性,PPV,NPV) 与成人衍生模型进行比较.
主要成果:
- 新型儿科模型在曲线下的测试面积 (AUC) 非常好:LVEF ≤35%的0.93和LVEF <50%的0.88.
- 对于LVEF<50%的儿科模型显示高灵敏度 (0.85) 和特异性 (0.80),负预测值为0.995.
- 一个用于RVSD检测的儿科模型的AUC达到0.90.
- 成人衍生模型表现良好 (LVEF的AUC为0.87 ≤35%),但小儿特异模型的表现优于其.
结论:
- 人工智能支持的心电图分析能够准确地检测儿科患者的LVSD和RVSD.
- 与成年人训练的模型相比,专门用于儿童的AI模型在儿童诊断这些疾病方面提供了更高的性能.
- 这些发现支持AI-ECG的临床实用性,用于早期识别儿科心脏功能障碍.
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