Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age
Open accessAbstract
BACKGROUND: Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine. OBJECTIVE: This study aimed to evaluate the performance of an artificial intelligence method based on automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses compared with the current formulas using standard fetal biometry. STUDY DESIGN: Standard transthalamic axial plane images from a total of 1394 patients undergoing routine fetal ultrasound were used to develop an artificial intelligence method to automatically estimate gestational age from the analysis of fetal brain information. We compared its performance-as stand alone or in combination with fetal biometric parameters-against 4 currently used fetal biometry formulas on a series of 3065 scans from 1992 patients undergoing second (n=1761) or third trimester (n=1298) routine ultrasound, with known gestational age estimated from crown rump length in the first trimester. RESULTS: Overall, 95% confidence interval of the error in gestational age estimation was 14.2 days for the artificial intelligence method alone and 11.0 when used in combination with fetal biometric parameters, compared with 12.9 days of the best method using standard biometrics alone. In the third trimester, the lower 95% confidence interval errors were 14.3 days for artificial intelligence in combination with biometric parameters and 17 days for fetal biometrics, whereas in the second trimester, the 95% confidence interval error was 6.7 and 7, respectively. The performance differences were even larger in the small-for-gestational-age fetuses group (14.8 and 18.5, respectively). CONCLUSION: An automated artificial intelligence method using standard sonographic fetal planes yielded similar or lower error in gestational age estimation compared with fetal biometric parameters, especially in the third trimester. These results support further research to improve the performance of these methods in larger studies.
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AI algorithms in fetal neurosonography enabled the accurate estimation of GA in a pregnancy without a valid first trimester scan.
“and the accurate estimation of GA in a pregnancy without a valid first trimester scan [95].”
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 [95]
The Gestational age estimation section reports that the AI method estimated GA, with greater accuracy when combined with fetal biometrics. But the Original Research section says estimates were evaluated against GA established by a first-trimester scan; the Conclusions present use in pregnancies without such dating as a direction for further research, not a demonstrated result. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBecause most studies use data from healthy subjects or healthy fetuses, miscalculation or misdiagnosis may occur in case of pathologies.
“As most of the studies are performed with data obtained from healthy subjects or healthy fetuses, miscalculation or misdiagnosis may occur in case of pathologies [35,95,145].”
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 [95]
The Study design section says this study excluded congenital fetal malformations and aneuploidies, and Strengths and limitations says the authors have no data on performance in fetuses with malformations (page numbers unavailable in our copy). The paper reports gestational-age estimation errors in small- and large-for-gestational-age fetuses, but does not establish that most studies use healthy fetuses or that malformations cause miscalculation or misdiagnosis. (LLM, conf=0.83)
Experimental — accuracy not yet measuredBurgos-Artizzu et al. and Sreelakshmy et al. portrayed AI methods for the estimation of GA through an analysis of transthalamic axial planes or cerebellum measurements.
“Lastly, the studies of Burgos-Artizzu et al. and Sreelakshmy et al. portrayed AI methods for the estimation of GA through an analysis of transthalamic axial planes or cerebellum measurements [95,96].”
A limitation of AI US imaging in fetal neurosonography was the missing training of AI algorithms with images of pathologies.
“Other study limitations were the missing training of AI algorithms with images of pathologies [79,86,95]”
Some AI models can fail in pattern recognition tasks when subtle differences are diagnosis-relevant, e.g., in borderline findings or in small regions of interest such as endometrial thickness or fetal brain structures.
“some AI models can fail when subtle differences are diagnosis-relevant, e.g., in borderline findings or in small regions of interest such as endometrial thickness or fetal brain structures [95,201,208].”
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 [95]
The Principal findings section says the AI model detected fetal-brain changes relevant to gestational-age estimation, while Strengths and limitations acknowledges residual dating error. Neither passage addresses failure to recognize subtle diagnosis-relevant differences in borderline findings or small regions of interest. Searches for those concepts and for pattern-recognition failures found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Vellvé, K., Eixarch, E., Crispi, F., Bonet-Carne, E., Bennasar, M., & Gratacos, E.
- Affiliations
- Hospital Sant Joan de Déu Barcelona (ES)
- Corresponding author
- Burgos-Artizzu, Xavier P.
- ORCID
- Venue
- American Journal of Obstetrics & Gynecology MFM
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 3
- Issue
- 6
- Pages
- 100462
- ISSN
- 2589-9333 (ISSN-L)
- OpenAlex
- W3194208371
- Cited by
- 39
- References
- 37
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 11 · 2023: 11 · 2022: 3 · 2021: 1
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- American Journal of Obstetrics & Gynecology MFM (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by)
- Funders
- Fundación La Caixa
- Fundación Cellex
- Instituto de Salud Carlos III (PI16/00861, PI17/00675)
- European Regional Development Fund
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Keywords
- gestational age estimation, fetal brain ultrasound, fetal brain maturation, artificial intelligence, fetal biometry, second trimester, third trimester, small-for-gestational-age fetuses, crown-rump length, prenatal care
- MeSH terms
- Artificial Intelligence, Brain, Female, Gestational Age, Humans, Pregnancy, Ultrasonography, Prenatal, Crown-Rump Length
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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