  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.
