人工智能启用心电图来检测先天纠正的大动脉的转移
Sunil J Ghelani1,2, Nikhil Thatte3, William La Cava4
1Department of Cardiology, Harvard Medical School, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA. sunil.ghelani@cardio.chboston.org.
Pediatric cardiology
|June 16, 2025
概括
开发了一种人工智能启用心电图 (AI-ECG) 模型,以检测L循环先天性纠正的大动脉转移 (ccTGA). 这种AI-ECG工具对早期诊断这种罕见的先天性心脏缺陷充满希望.
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:
- 大动脉L-循环先天性纠正转移 (ccTGA) 是一种罕见的先天性心脏缺陷.
- ccTGA经常在长时间内没有被诊断出来,导致患者患病率显著.
- 早期发现ccTGA对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和内部验证一个支持人工智能的心电图 (AI-ECG) 模型,用于诊断ccTGA.
- 为了利用标准的12导心电图进行非侵入性ccTGA检测,使用AI.
- 识别微妙的心电图图案,表明ccTGA,用于早期诊断查.
主要方法:
- 一个卷积神经网络 (CNN) 模型在61,482名患者初始心电图的数据集上受过训练.
- 数据集被分为训练 (70%) 和测试 (30%) 队列,0.7%的患者被诊断患有ccTGA.
- 模型性能使用接收器运行特征曲线下的面积 (AUC-ROC) 和精度回忆曲线下的面积 (AUC-PR) 来评估.
主要成果:
- 在AI-ECG模型中,AUC-ROC为0.95 (95%CI为0.94-0.96) 和AUC-PR为0.16 (95%CI为0.12-0.21).
- 该模型在各种年龄组中表现出强的性能,在一个月以下的婴儿的准确性略有下降.
- 人工智能识别的关键诊断特征包括扩大QRS复合体,V1-V2引线中的特定负QRS模式,以及侧面前引线中没有Q波.
结论:
- AI-ECG技术可以有效地检测与罕见的先天性心脏缺陷相关的复杂模式,如ccTGA.
- 这种AI-ECG模型为早期ccTGA诊断提供了一个可扩展的解决方案,有可能改善获得专业心脏护理的机会.
- 建议进一步研究涉及外部验证和多模式AI模型,以提高临床效用.
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