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

arXiv: https://arxiv.org/abs/2401.01201 · HTML: https://mlsanity.com/paper/2401.01201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1804.09102/) (2018)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)

## Built on by (most cited first, 1 of 1)

- [A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry](https://mlsanity.com/paper/2512.16710/) (2025)

## Abstract (arXiv)

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.