ArticleNPJ digital medicine2025
Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A Systematic Review of the Use of Artificial Intelligence Systems for Assessing Multiple Parameters During the Ultrasound Evaluation of Fetal Growth.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Obstetrics: Current Trends and Future Directions.Journal of clinical medicine · 2026Review
- Optimized fetal head circumference estimation in 2D ultrasound using EfficientNet-B7 and Adam optimizer.BMC pediatrics · 2026Article
- A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry.Scientific reports · 2026Article
- Artificial Intelligence in Obstetrics and Gynaecology: Advancing Precision and Personalised Care.Cureus · 2025Review
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review.Frontiers in medicine · 2025Review
- Diagnosis and Early Management of Robin Sequence.Children (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.