No sonographer, no radiologist: New system for automatic prenatal detection of fetal biometry, fetal presentation, and placental location
Open accessAbstract
Ultrasound imaging is a vital component of high-quality Obstetric care. In rural and under-resourced communities, the scarcity of ultrasound imaging results in a considerable gap in the healthcare of pregnant mothers. To increase access to ultrasound in these communities, we developed a new automated diagnostic framework operated without an experienced sonographer or interpreting provider for assessment of fetal biometric measurements, fetal presentation, and placental position. This approach involves the use of a standardized volume sweep imaging (VSI) protocol based solely on external body landmarks to obtain imaging without an experienced sonographer and application of a deep learning algorithm (U-Net) for diagnostic assessment without a radiologist. Obstetric VSI ultrasound examinations were performed in Peru by an ultrasound operator with no previous ultrasound experience who underwent 8 hours of training on a standard protocol. The U-Net was trained to automatically segment the fetal head and placental location from the VSI ultrasound acquisitions to subsequently evaluate fetal biometry, fetal presentation, and placental position. In comparison to diagnostic interpretation of VSI acquisitions by a specialist, the U-Net model showed 100% agreement for fetal presentation (Cohen’s κ 1 (p<0.0001)) and 76.7% agreement for placental location (Cohen’s κ 0.59 (p<0.0001)). This corresponded to 100% sensitivity and specificity for fetal presentation and 87.5% sensitivity and 85.7% specificity for anterior placental location. The method also achieved a low relative error of 5.6% for biparietal diameter and 7.9% for head circumference. Biometry measurements corresponded to estimated gestational age within 2 weeks of those assigned by standard of care examination with up to 89% accuracy. This system could be deployed in rural and underserved areas to provide vital information about a pregnancy without a trained sonographer or interpreting provider. The resulting increased access to ultrasound imaging and diagnosis could improve disparities in healthcare delivery in under-resourced areas.
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AI is of public health relevance by reducing costs and the need for sonography experts.
“It, therefore, is of public health relevance, by reducing costs and the need for sonography experts [35,39].”
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 [39]
The Introduction and Discussion say the proposed AI-assisted approach could reduce reliance on experienced sonographers and interpreting specialists, improving access in under-resourced areas. They describe it as resource-conscious but do not establish that it reduces costs; costly ultrasound devices remain a stated obstacle. No page numbers are provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredArroyo et al. assessed AI for fetal presentation, placental location and fetal biometry for estimation of gestational age using standardized ultrasound sweeps obtained in the absence of trained sonographers.
“Arroyo et al. assessed the use of AI for fetal presentation, placental location and fetal biometry for estimation of gestational age using standardized ultrasound sweeps obtained in the absence of trained sonographers.33”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [33]
AI usage in standard plane acquisition has the benefit of feasibility of biometric assessment by non-experts.
“the feasibility of biometric assessment by non-experts [35,39,43]”
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- Authors
- Arroyo, J., Marini, T. J., Saavedra, A. C., Toscano, M., Baran, T. M., Drennan, K., Dozier, A., Zhao, Y. T., Egoavil, M., Tamayo, L., Ramos, B., & Castaneda, B.
- Affiliations
- Pontificia Universidad Católica del Perú (PE)
- University of Rochester Medicine (US)
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-02-09
- Volume
- 17
- Issue
- 2
- Pages
- e0262107
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC8827457
- OpenAlex
- W4210845137
- Cited by
- 40
- References
- 31
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 6 · 2023: 7 · 2022: 9
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Innóvate Perú (409- FIDECOM INNOVATEPERU-PVE-2017)
- Pontificia Universidad Católica del Perú (Períodos de Investigación 2020)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Keywords
- fetal biometry, fetal presentation, placental location, U-Net segmentation, fetal head segmentation, gestational age estimation, biparietal diameter, head circumference
- MeSH terms
- Deep Learning, Adult, Biometry, Female, Fetus, Gestational Age, Humans, Labor Presentation, Peru, Placenta, Pregnancy, Ultrasonography, Prenatal, Young Adult
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