Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
This provides a fully automated, operator-free solution for fetal anomaly screening, potentially improving efficiency and consistency in prenatal care.
The paper tackles the problem of automating fetal biometric measurements from 20-week ultrasound scans by aggregating data from every frame of a scan, achieving human-level performance with well-calibrated credible intervals.
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.