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神经网络集成可提供复杂先天性心脏病的专家级产前检测
作者:小柯机器人 发布时间:2021/5/18 15:22:43

美国加州大学旧金山分校Rima Arnaout团队发现,神经网络集成可提供复杂先天性心脏病的专家级产前检测。相关论文于2021年5月14日发表于国际学术期刊《自然—医学》。

通过使用来自1,326例回顾性超声心动图的107,823张图像并筛选了18至24周胎儿的超声,研究人员训练了一组神经网络来鉴别心脏视图,并区分正常心脏和复杂冠心病。研究人员还使用了分割模型来计算标准的胎儿心胸测量值。在对4,108份胎儿调查的内部测试集中(0.9%的冠心病,超过440万张图像),该模型在用于区分正常心脏和异常心脏时获得了0.99的曲线下面积(AUC),95%的敏感性(95%的置信区间(CI),84–99% ),96%的特异性(95%CI,95–97%)和100%的阴性预测值。

模型的敏感性与临床医生相当,并且在医院外和较低质量的图像上仍然很可靠。该模型的决策基于临床相关特征。心脏测量值与报告的正常和异常心脏测量值相关。将集成学习模型应用于指南推荐的影像学可以显著改善胎儿冠心病的检测,这是一个关键且全面的诊断挑战。

据悉,先天性心脏病是最常见的先天性缺陷。胎儿筛查超声可以提供五种心脏视图,它们可以一起检测出90%的复杂冠心病,但在实践中,灵敏度低至30%。

附:英文原文

Title: An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease

Author: Rima Arnaout, Lara Curran, Yili Zhao, Jami C. Levine, Erin Chinn, Anita J. Moon-Grady

Issue&Volume: 2021-05-14

Abstract: Congenital heart disease (CHD) is the most common birth defect. Fetal screening ultrasound provides five views of the heart that together can detect 90% of complex CHD, but in practice, sensitivity is as low as 30%. Here, using 107,823 images from 1,326 retrospective echocardiograms and screening ultrasounds from 18- to 24-week fetuses, we trained an ensemble of neural networks to identify recommended cardiac views and distinguish between normal hearts and complex CHD. We also used segmentation models to calculate standard fetal cardiothoracic measurements. In an internal test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images), the model achieved an area under the curve (AUC) of 0.99, 95% sensitivity (95% confidence interval (CI), 84–99%), 96% specificity (95% CI, 95–97%) and 100% negative predictive value in distinguishing normal from abnormal hearts. Model sensitivity was comparable to that of clinicians and remained robust on outside-hospital and lower-quality images. The model’s decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guideline-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD, a critical and global diagnostic challenge.

DOI: 10.1038/s41591-021-01342-5

Source: https://www.nature.com/articles/s41591-021-01342-5

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex