ArticleNPJ digital medicine2023
Machine learning for accurate estimation of fetal gestational age based on ultrasound images.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled 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
- AI-Driven Three-Dimensional Reconstruction of Fetal Cardiovascular Pathways from Ultrasound Sweep Videos for Congenital Heart Disease Screening.Bioengineering (Basel, Switzerland) · 2026Article
- 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
- 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
- Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging.Pediatric radiology · 2026Article
- A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry.Scientific reports · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Artificial Intelligence-Based Automated Assessment of the Four-Chamber View in Fetal Cardiac Ultrasound Videos.Bioengineering (Basel, Switzerland) · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video.NPJ digital medicine · 2025Article
- The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance.NPJ digital medicine · 2025Article
- Predicting abnormal fetal growth using deep learning.NPJ digital medicine · 2025Article
- Accuracy of Transverse Cerebellar Diameter in Estimating Gestational Age in the Second and Third Trimester: A Prospective Study in Saudi Arabia.Diagnostics (Basel, Switzerland) · 2025Article
- Overcoming barriers in the use of artificial intelligence in point of care ultrasound.NPJ digital medicine · 2025Review
- General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.Nature communications · 2025Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Etiologically Based Functional Taxonomy of the Preterm Birth Syndrome.Clinics in perinatology · 2024Review
- Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.Reproductive biology and endocrinology : RB&E · 2024Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks' gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9-3.2) and 4.3 (95% CI, 4.1-4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.
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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.