对于产前先天性心脏病查的深度学习模型一般化到社区环境中,并且优于临床检测
C Athalye1, A van Nisselrooij2, S Rizvi1
1Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
概括
深度学习模型在改善先天性心脏缺陷 (CHD) 的产前检测方面表现有前途. 这种人工智能方法表现出比初步临床评估更高的灵敏度,即使是在以前未见过的病例上也是如此.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胎儿医学 胎儿医学
背景情况:
- 先天性心脏缺陷 (CHD) 是婴儿发病率和死亡率的重要原因.
- 尽管产前超声波查广泛,但在例行检查中,大量的心血管疾病病例被遗漏.
- 深度学习 (DL) 为医学诊断中的自动图像分析提供了潜力.
研究的目的:
- 评估深度学习 (DL) 模型在检测胎儿先天性心脏缺陷 (CHD) 的性能,使用第二个三个月的超声图像.
- 为了比较DL模型的诊断准确度与初始临床诊断和专家人类审查员.
- 评估DL模型在低风险人群的图像和以前未经发现的心脏病变上的有效性.
主要方法:
- 胎儿超声波图像的回顾性分析,从怀孕中与孤立的严重心血管疾病和正常对照进行.
- 在训练有素的DL模型,初始的临床诊断诊断和盲目的专家临床医生之间对诊断准确性的比较.
- 基于图像质量,研究持续时间和特定查视图的可用性进行性能分析.
主要成果:
- DL模型实现了91%的灵敏度和78%的特异性,明显超过了最初的临床诊断 (53%的灵敏度).
- 盲人人类专家的平均灵敏度为55%,特异性为71%.
- DL模型甚至在心脏病变中表现出强的性能,而这些心脏病变并未包含在其训练数据集中.
结论:
- 与最初的临床评估相比,深度学习算法显著提高了先天性心脏缺陷的检测率.
- DL模型在社区获取的低风险人群图像上显示出高准确度,包括新的病变.
- 这些发现表明,DL模型可以增强产前心血管疾病查,可能减少错过诊断.
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