慢性肝病的机会性查与深度学习增强心声学
Yuki Sahashi1, Milos Vukadinovic1,2, Fatemeh Amrollahi3
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles.
NEJM AI
|October 6, 2025
概括
一个新的深度学习工具,EchoNet-Liver,使用心声学查慢性肝病 (CLD),包括脂肪性肝病 (SLD) 和肝硬化. 这项技术提供机会性检测,识别可能需要进一步诊断评估和治疗的患者.
科学领域:
- 医疗成像中的人工智能
- 心血管成像 - 心血管成像
- 肝病学 肝病学是一种肝病学.
背景情况:
- 慢性肝病 (CLD) 影响超过15亿成年人,往往保持无症状和未诊断.
- 心声学,一种常见的成像方式,捕获肝脏可视化,目前尚未用于CLD诊断.
研究的目的:
- 开发和评估EchoNet-Liver,这是一个深度学习管道,用于从心声图中识别CLD.
- 评估模型检测肝硬化和脂肪性肝病 (SLD) 的能力,使用皮下心声图像.
主要方法:
- 一项回顾性观察性研究,利用两个学术医疗中心的成年患者的回声心电图数据.
- 开发计算机视觉管道 (EchoNet-Liver) 以提取相关的肝脏图像并预测肝硬化和SLD.
- 模型预测与来自对腹部超声波或MRI研究的诊断的比较.
主要成果:
- 在一个持久测试队列中,EchoNet-Liver在检测肝硬化症 (AUROC 0.837) 和SLD (AUROC 0.799) 中表现出强的表现.
- 该模型在对配对MRI研究进行测试时,在肝硬化时达到0.704的AUROC,在SLD时达到0.725.
- 在单独的队列中进行的外部验证证实了该模型的有效性,AUROC为0.830的肝硬化和0.769的SLD.
结论:
- 对心声回声图的深度学习分析有助于对SLD和肝硬化进行机会性查.
- EchoNet-Liver可以识别那些可能从进一步的CLD诊断测试和治疗中受益的患者.
- 这种方法将肝病查与常规心脏评估相结合.
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