Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans

• URL: http://arxiv.org/abs/2401.01201v1
• Date: Tue, 2 Jan 2024 13:04:41 GMT
• Title: Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
• Authors: Lorenzo Venturini, Samuel Budd, Alfonso Farruggia, Robert Wright, Jacqueline Matthew, Thomas G. Day, Bernhard Kainz, Reza Razavi, Jo V. Hajnal
• Abstract summary: We introduce a paradigm shift that attains human-level performance in biometric measurement. We use a convolutional neural network to classify each frame of an ultrasound video recording. We measure fetal biometrics in every frame where appropriate anatomy is visible.
• Score: 6.1751261266833986
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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