ArticleNPJ digital medicine2025
Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video.
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 11 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
11 citing papers in PubMed.
- Real-time gestational-age estimation at 11-13 weeks using artificial intelligence on non-targeted ultrasound sweeps.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- 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
- Article
- Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.Abdominal radiology (New York) · 2026Article
- Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.Abdominal radiology (New York) · 2026Article
- Review
- A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry.Scientific reports · 2026Article
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- Ultrasonographic fetal sex determination in large domestic animals: a comparative, mechanistic, and field-oriented synthesis.Frontiers in veterinary science · 2026Review
- Artificial intelligence and the future of maternal and newborn health in low-income countries: advancing equity, early detection, and health system resilience.Frontiers in public health · 2026Review
- Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol.Reproductive health · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We developed a deep learning model trained on over two million ultrasound images from 78,531 pregnancies from Australia, India, and the UK to estimate gestational age (GA) directly from any fetal ultrasound image, regardless of orientation. The model outputs both a GA estimate and an uncertainty value based on image quality. Independent validation on 36,762 ultrasound images from 742 fetuses showed a mean absolute error (MAE) of 1.7 days at 14-18 weeks and 2.8 days at 18-24 weeks, significantly outperforming traditional biometry (p < 0.001). In video analysis, the model achieved a median prediction time of 24 s and an MAE below 3 days across all trimesters. Performance was consistent across maternal body mass index (BMI) categories and geographic settings. This AI-based GA estimation method matches or exceeds gold-standard fetal biometry, reduces reliance on highly skilled sonologists, and offers the potential to improve access to prenatal care in resource-limited and underserved settings globally.
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.