Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis
Open accessAbstract
Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.
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Maraci et al. successfully showed automated head plane detection and GA estimation with point-of-care devices.
“The recent topic of point-of-care-US was addressed in the research of Maraci et al., who successfully showed automated head plane detection and GA estimation with point-of-care devices [151].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [151]
The Experimental Evaluation reports automated transcerebellar head-plane detection and gestational-age estimation, including a first test on one video from a low-cost probe (Section 3.4; page unavailable). But that test had a high false-positive rate for plane detection and underestimated the diameter used for gestational-age estimation. The Discussion describes the components as a proof of principle requiring further work for clinically accepted results, so the claim overstates what was successfully shown with point-of-care devices. (LLM, conf=0.90)
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- Authors
- Maraci, M. A., Yaqub, M., Craik, R., Beriwal, S., Self, A., von Dadelszen, P., Papageorghiou, A., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- King's College London (GB)
- King's College London
- ORCID
- Venue
- Journal of medical imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2020-01-13
- Volume
- 7
- Issue
- 01
- Pages
- 1-9
- ISSN
- 2329-4302 (ISSN-L)
- 2329-4310
- PMC
- PMC6956669
- OpenAlex
- W3000423265
- Cited by
- 36
- References
- 44
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 4 · 2023: 5 · 2022: 11 · 2021: 2 · 2020: 3
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of medical imaging (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- National Institute for Health Research Oxford Biomedical Research Centre
- Engineering and Physical Sciences Research Council (EP/G036861/1, EP/M013774/1, EP/K503769/1, EP/L505316/1, EP/R013853/1)
- Research Council UK Digital Economy (EP/G036861/1)
- European Research Council (Advanced Grant 694581)
- Bill and Melinda Gates Foundation (GCE Project 1128591)
- Medical Research Council (MR/P027938/1, 501100000265)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- point-of-care ultrasound, gestational age estimation, prenatal ultrasound, automated measurement, convolutional neural networks, low- and middle-income countries, prenatal care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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