儿童右心室评估的AI学习:在多个中心进行开发和验证
medRxiv : the preprint server for health sciences
|March 31, 2025
概括
一个人工智能工具自动化了儿童的右心室 (RV) 功能评估,实现了专家级准确性. 这项技术改善了儿童心脏病的诊断和管理,特别是在服务不足的地区.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 儿科心血管健康 儿科心血管健康
背景情况:
- 在全球范围内,先天性和获得性心脏病影响着大约1%的儿童,右心室 (RV) 功能障碍构成了重大的临床挑战.
- 在儿科患者中进行准确的RV评估是复杂的,因为独特的心脏几何形状,腹腔间相互作用和形态变异性.
- 分数面积变化 (FAC) 是评估RV功能严重程度和指导儿科心脏病治疗的关键心声学指标.
研究的目的:
- 开发和验证人工智能 (AI) 框架,用于在儿科心声图中进行自动 RV 评估.
- 提高RV函数量化的准确性和标准化,包括小数面积变化 (FAC).
- 扩大人工智能框架的能力,以改善左心室 (LV) 功能评估和心脏异常识别,如肺高血压 (PH).
主要方法:
- 利用了来自北美和亚洲三级保健中心的3,993名儿科患者的24,984个心声回声图的大数据集.
- 开发了一种多任务学习人工智能框架,用于自动化RV细分,节拍RV FAC量化和PH识别.
- 验证了AI框架的性能与专家注释以及不同患者队伍的表现.
主要成果:
- 人工智能系统实现了VR细分的高 Dice相似系数 (0.86 A4C,0.88 PSAX),相当于专家的性能.
- 证明了强大的RV功能评估,曲线下的面积 (AUC) 值为0.95 (美国) 和0.97 (亚洲队列).
- 实现了PH分类 (0.95美国,0.94亚洲) 的高诊断准确度,并显著改善了LV射出分数 (EF) 的预测.
结论:
- 经过验证的AI框架为儿科患者提供了专家级别的可靠,自动化的心室功能分析.
- 这项技术有可能简化临床工作流程和标准化心脏评估,改善儿童心血管疾病的护理.
- 人工智能框架为改善资源有限的环境中的儿科心脏护理提供了特殊的好处.
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