Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software
Open accessAbstract
Purpose To evaluate the feasibility and reproducibility of artificial intelligence software (Smartplanes®) to automatically identify the transthalamic plane from 3D ultrasound volumes and to measure the biparietal diameter (BPD) and head circumference (HC) in fetus.Material and methods Thirty fetuses were evaluated at 17-30 weeks' gestation. For each fetus two three-dimensional (3D) volumes of the fetal head along with one conventional two-dimensional (2D) image of the transthalamic plane were prospectively acquired. The Smartplanes® software identified the transthalamic plane from the 3D volumes and performed BPD and HC measurements automatically (3D auto). Two experienced sonographers also measured BPD and HC from 2D images and from the 3D volumes. Measurements were compared using Bland-Altman plots. Interclass correlation coefficient (ICC) was used to evaluate intra- and interobserver reproducibility.Results For each series of measurements, intra- and interobserver reproducibility rates were high with ICC values>0.98. The 95% confidence intervals between the BPD measurements were 2mm (3D versus 2D) and 4mm (3D auto versus 2D) and the HC measurements were 7.5mm (3D versus 2D) and 11mm (3D auto versus 2D).Conclusion Fetal head measurements obtained automatically by Smartplanes® software from 3D volumes show good agreement with those obtained by two experienced sonographers from conventional 2D images and 3D volumes. The reproducibility of these measurements is similar to that observed by experienced sonographers.
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Only three of the included studies investigated the use of AI algorithms in 3D US images, while 24 focused on 2D images.
“Only three of the included studies investigated the use of AI algorithms in 3D US images [22,23,24], while 24 focused on 2D images.”
A limitation of AI applications for fetal biometry is reduced algorithm accuracy in poor quality images due to high maternal BMI.
“reduced algorithm accuracy in poor quality images due to high maternal BMI [22,23]”
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References 8 · Cited by 57
- Authors
- Ambroise Grandjean, G., Hossu, G., Bertholdt, C., Noble, P., Morel, O., & Grangé, G.
- Affiliations
- Inserm (FR)
- Hôpital Cochin (FR)
- Venue
- Diagnostic and Interventional Imaging
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 99
- Issue
- 11
- Pages
- 709-716
- ISSN
- 2211-5684 (ISSN-L)
- OpenAlex
- W2889222893
- Cited by
- 57
- References
- 8
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 9 · 2023: 10 · 2022: 5 · 2021: 13 · 2020: 7 · 2019: 5
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Diagnostic and Interventional Imaging (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- artificial intelligence, biparietal diameter, head circumference, 3D ultrasound, automated measurement, reproducibility
- MeSH terms
- Artificial Intelligence, Cephalometry, Feasibility Studies, Female, Fetus, Humans, Pregnancy, Software, Reproducibility of Results, Ultrasonography, Prenatal
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