用于儿科右心室评估的人工智能学习:在多个中心开发和验证
Charitha Reddy1, Yi Yan2, Min Qiu3
1Stanford University School of Medicine, Stanford, CA, USA. reddyc@stanford.edu.
NPJ digital medicine
|December 9, 2025
概括
一个新的AI工具准确地从心声图中评估儿科心脏功能,改善先天性心脏病和其他疾病的诊断. 这种自动化系统提高了一致性,并支持全球儿童早期治疗.
科学领域:
- 儿童心脏病学 儿童心脏病学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 右心室 (RV) 功能障碍是儿科心脏病的一个复杂问题,影响全球约1%的儿童.
- 由于解剖学变异性和不规则几何形状,对儿童进行准确的RV评估具有挑战性.
- 对于儿科患者的RV评估现有的方法缺乏一致性和效率.
研究的目的:
- 开发和验证一个深度学习框架,用于自动化RV功能评估儿科患者.
- 为了实现实时,专家级量化儿科心室功能.
- 改善诊断的一致性,并支持儿童心脏病早期干预.
主要方法:
- 利用了来自北美和亚洲的3993名儿童的24984个心声回声图的数据集.
- 开发了一个基于视频的深度学习框架,使用U2-Net架构进行自动心室细分和功能评估.
- 评估了模型在级细分,部分面积变化 (FAC) 估计,RV疾病分类和左心室喷射率 (LV EF) 预测方面的表现.
主要成果:
- 实现了高的细分精度 (子=0.86 [A4C],0.88 [PSAX]).
- 证明了强大的RV疾病分类性能 (AUC = 0.95美国,0.97亚洲).
- 在不同队列的LV EF预测中表现优于以前的方法.
结论:
- 经过验证的深度学习框架提供专家级,实时量化儿科心室功能.
- 这种自动化方法提高了诊断的一致性,并减少了手工工作量.
- 该工具有可能支持对患有心脏病的儿童的早期干预,特别是在资源有限的环境中.
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