ArticleCommunications medicine2022
A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- 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
- Article
- Training Frontline Providers in Basic Obstetric Ultrasound to Promote Healthy Pregnancy in Zimbabwe: Trainer and Trainee Experiences from a Qualitative Phenomenological Study.International journal of environmental research and public health · 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
- 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
- Evaluating the usability and practicality of AI-enabled smartphone-based obstetric ultrasound in Sierra Leone: a mixed-methods study.BMC pregnancy and childbirth · 2026Article
- Optimized fetal head circumference estimation in 2D ultrasound using EfficientNet-B7 and Adam optimizer.BMC pediatrics · 2026Article
- Innovation, Technology, and Telemedicine in Critical Care.Critical care clinics · 2026Review
- The application of artificial intelligence in blind ultrasound sweep diagnostics for prenatal medicine: A systematic literature review.Acta obstetricia et gynecologica Scandinavica · 2026Review
- Using artificial intelligence as a technological tool in gynecologic and obstetric health: A narrative literature review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video.NPJ digital medicine · 2025Article
- AI supported diagnostic innovations for impact in global women's health.BMJ (Clinical research ed.) · 2025Article
- 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
- Artificial Intelligence in Obstetrics and Gynaecology: Advancing Precision and Personalised Care.Cureus · 2025Review
- Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol.Reproductive health · 2025Article
- A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.Scientific reports · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters.The Lancet regional health. Southeast Asia · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings. Methods: Blind sweep ultrasounds, consisting of six freehand ultrasound sweeps, were collected by sonographers in the USA and Zambia, and novice operators in Zambia. We developed artificial intelligence (AI) models that used blind sweeps to predict gestational age (GA) and fetal malpresentation. AI GA estimates and standard fetal biometry estimates were compared to a previously established ground truth, and evaluated for difference in absolute error. Fetal malpresentation (non-cephalic vs cephalic) was compared to sonographer assessment. On-device AI model run-times were benchmarked on Android mobile phones. Results: Here we show that GA estimation accuracy of the AI model is non-inferior to standard fetal biometry estimates (error difference -1.4 ± 4.5 days, 95% CI -1.8, -0.9, Conclusions: The gestational age model is non-inferior to the clinical standard and the fetal malpresentation model has high AUC-ROCs across operators and devices. Our AI models are able to run on-device, without internet connectivity, and provide feedback scores to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.
Indexed as
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.