The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a convolutional neural network to classify each frame of an ultrasound video recording. We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. We performed a retrospective experiment on 1457 recordings (comprising 48 million frames) of 20-week ultrasound scans, estimated fetal biometrics in those scans and compared our estimates to the measurements sonographers took during the scan. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals in which the true biometric value is expected to lie.

翻译：当前胎儿异常筛查方法基于从单独选择的超声图像中获取的生物特征测量值。本文提出一种范式转变，通过聚合整个扫描过程中每一帧自动提取的生物特征，实现与人类水平相当的生物特征测量性能，且无需操作员干预。我们采用卷积神经网络对超声视频记录中的每一帧进行分类，随后在可见相应解剖结构的每一帧中测量胎儿生物特征。利用贝叶斯方法从大量测量值中估计各生物特征的真实值，并以概率方式剔除异常值。我们对1457份20周超声检查记录（包含4800万帧）开展回顾性实验，估计这些扫描中的胎儿生物特征，并将估计结果与超声技师在扫描过程中测量的数值进行比较。我们的方法在估计胎儿生物特征方面达到人类水平，并能够良好校准地估计真实生物特征值可能所在的置信区间。