Whole-examination AI estimation of fetal biometrics from 20-week
  ultrasound scans
Lorenzo Venturini Samuel Budd Alfonso Farruggia Robert Wright Jacqueline Matthew Thomas G. Day Bernhard Kainz Reza Razavi Jo V. Hajnal
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.