ArticleNEJM evidence2022
AI Estimation of Gestational Age from Blind Ultrasound Sweeps in Low-Resource Settings.
Article in NEJM evidence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Teleultrasound in obstetrics: A systematic review and meta-analysis.PLoS medicine · 2026Pooled it
- Systematic Review of Commercially Available Clinical CMUT-Based Systems for Use in Medical Ultrasound Imaging: Products, Applications, and Performance.Sensors (Basel, Switzerland) · 2025Pooled it
- Use of artificial intelligence for gestational age estimation: a systematic review and meta-analysis.Frontiers in global women's health · 2025Pooled it
- 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
- Artificial Intelligence in Obstetrics: Current Trends and Future Directions.Journal of clinical medicine · 2026Review
- The FemTech revolution: Unlocking the potential of new technology for optimizing pregnancy outcomes in low- and middle-income countries and remote areas.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography.JAMA network open · 2026Article
- Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- The application of artificial intelligence in blind ultrasound sweep diagnostics for prenatal medicine: A systematic literature review.Acta obstetricia et gynecologica Scandinavica · 2026Review
- AI-assisted point of care ultrasound and an innovative vaginal speculum: a proposed combined approach to reducing spontaneous preterm birth.Frontiers in medicine · 2026Article
- 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
- Article
- AI-enabled obstetric point-of-care ultrasound as an emerging technology in low- and middle-income countries: provider and health system perspectives.BMC pregnancy and childbirth · 2025Article
- Evaluating the digital health technology landscape in sub-Saharan Africa and its implications for cardiovascular health.NPJ cardiovascular health · 2025Review
- Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol.Reproductive health · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.Scientific reports · 2025Article
- Limited echocardiogram acquisition by novice clinicians aided with deep learning: A randomized controlled trial.Biology methods & protocols · 2025Article
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundUltrasound is indispensable to gestational age estimation and thus to quality obstetrical care, yet high equipment cost and the need for trained sonographers limit its use in low-resource settings.
methodsFrom September 2018 through June 2021, we recruited 4695 pregnant volunteers in North Carolina and Zambia and obtained blind ultrasound sweeps (cineloop videos) of the gravid abdomen alongside standard fetal biometry. We trained a neural network to estimate gestational age from the sweeps and, in three test data sets, assessed the performance of the artificial intelligence (AI) model and biometry against previously established gestational age.
resultsIn our main test set, the mean absolute error (MAE) (±SE) was 3.9±0.12 days for the model versus 4.7±0.15 days for biometry (difference, -0.8 days; 95% confidence interval [CI], -1.1 to -0.5; P<0.001). The results were similar in North Carolina (difference, -0.6 days; 95% CI, -0.9 to -0.2) and Zambia (-1.0 days; 95% CI, -1.5 to -0.5). Findings were supported in the test set of women who conceived by in vitro fertilization (MAE of 2.8±0.28 vs. 3.6±0.53 days for the model vs. biometry; difference, -0.8 days; 95% CI, -1.7 to 0.2) and in the set of women from whom sweeps were collected by untrained users with low-cost, battery-powered devices (MAE of 4.9±0.29 vs. 5.4±0.28 days for the model vs. biometry; difference, -0.6; 95% CI, -1.3 to 0.1).
conclusionsWhen provided blindly obtained ultrasound sweeps of the gravid abdomen, our AI model estimated gestational age with accuracy similar to that of trained sonographers conducting standard fetal biometry. Model performance appears to extend to blind sweeps collected by untrained providers in Zambia using low-cost devices. (Funded by the Bill and Melinda Gates Foundation.).
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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.