Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans
Venturini L, Budd S, Farruggia A, Wright R, Matthew J, Day TG, Kainz B, Razavi R, Hajnal JV (2025)
Publication Type: Journal article
Publication year: 2025
Journal
Book Volume: 8
Pages Range: 1--12
Article Number: 22
Issue: 1
Journal Issue: 1
DOI: 10.1038/s41746-024-01406-z
Abstract
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 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 real-time manual measurements. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals for the true biometric value.
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United Kingdom (GB)How to cite
APA:
Venturini, L., Budd, S., Farruggia, A., Wright, R., Matthew, J., Day, T.G.,... Hajnal, J.V. (2025). Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans. npj Digital Medicine, 8(1), 1--12. https://doi.org/10.1038/s41746-024-01406-z
MLA:
Venturini, Lorenzo, et al. "Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans." npj Digital Medicine 8.1 (2025): 1--12.
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