Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry
Open accessAbstract
OBJECTIVES: To evaluate the accuracy of an automated three-dimensional (3D) ultrasound technique for fetal intracranial measurements compared with manual acquisition. METHODS: This was a prospective observational study of patients presenting for routine anatomical survey between 18 + 0 and 22 + 6 weeks' gestation. After providing informed consent, each patient underwent two consecutive ultrasound examinations of the fetal head, one by a sonographer and one by a physician. Each operator obtained manual measurements of the biparietal diameter (BPD), head circumference (HC), transcerebellar diameter (TCD), cisterna magna (CM) and posterior horn of the lateral ventricle (Vp), followed by automated measurements of these structures using an artificial intelligence-based tool, SonoCNS® Fetal Brain. Both operators repeated the automated approach until all five measurements were obtained in a single sweep, up to a maximum of three attempts. The accuracy of automated measurements was compared with that of manual measurements using intraclass correlation coefficients (ICC) by operator type, accounting for patient and ultrasound characteristics. RESULTS: ) and median subcutaneous thickness was 1.6 cm (IQR, 1.3-2.0 cm). Fifteen (10%) patients had at least one prior Cesarean delivery, 17 (12%) had other abdominal surgery and 78 (55%) had an anterior placenta. Successful acquisition of the automated measurements was achieved on the first, second and third attempts in 70%, 22% and 3% of patients, respectively, by sonographers and in 76%, 16% and 3% of cases, respectively, by physicians. The automated algorithm was not able to identify and measure all five structures correctly in six (4%) and seven (5%) patients scanned by the sonographers and physicians, respectively. The ICCs reflected good reliability (0.80-0.88) of the automated compared with the manual approach for BPD and HC and poor to moderate reliability (0.23-0.50) for TCD, CM and Vp. Fetal lie, head position, placental location, maternal subcutaneous thickness and prior Cesarean section were not associated with the success or accuracy of the automated technique. CONCLUSIONS: Automated 3D ultrasound imaging of the fetal head using SonoCNS reliably identified and measured BPD and HC but was less consistent in accurately identifying and measuring TCD, CM and Vp. While these results are encouraging, further optimization of the automated technology is necessary prior to incorporation of the technique into routine sonographic protocols. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.
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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]”
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 [23]
The Discussion (page not specified) says accuracy might decrease at higher maternal BMI, but the adjusted model did not find BMI to predict accuracy. It does not establish that high BMI caused poor-quality images or reduced algorithm accuracy, as the citing sentence states. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOnly 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.”
AI usage in standard plane acquisition has the benefit of incorporation of clinical aspects into image interpretation.
“the incorporation of clinical aspects into image interpretation [21,23,26]”
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 [23]
The Introduction describes AI-based alignment and measurement of fetal-brain images, while the Discussion describes AI as a supplement to clinicians’ judgment. Neither section says that standard-plane acquisition incorporates clinical aspects into image interpretation (searched for that phrase and for clinical context in image interpretation). (LLM, conf=0.69)
Experimental — accuracy not yet measuredWhen contextual clinical information is additionally incorporated in the AI model, the level of misclassification and misdiagnosis has been shown to be reduced.
“when contextual clinical information is additionally incorporated in the AI model, the level of misclassification and misdiagnosis has been shown to be reduced [23,26].”
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 [23]
The Methods and Discussion assess whether patient and ultrasound characteristics affect the accuracy of automated fetal-head measurements; they do not describe incorporating contextual clinical information into the AI model. The Discussion reports no significant predictors of accuracy, but the paper does not test whether adding such information reduces misclassification or misdiagnosis. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Pluym, I. D., Afshar, Y., Holliman, K., Kwan, L., Bolagani, A., Mok, T., Silver, B., Ramirez, E., Han, C. S., & Platt, L. D.
- Affiliations
- University of California, Los Angeles (US)
- Center for Fetal Medicine and Women's Ultrasound Los Angeles CA USA
- Corresponding author
- Pluym, I. D.
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2021-05
- Volume
- 57
- Issue
- 5
- Pages
- 798-803
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W3048235968
- Cited by
- 46
- References
- 29
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10 · 2023: 11 · 2022: 5 · 2021: 6
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- biparietal diameter, head circumference, transverse cerebellar diameter, cisterna magna, artificial intelligence in ultrasound, measurement accuracy, manual vs automated measurement, placental location, previous cesarean section
- MeSH terms
- Adult, Artificial Intelligence, Biometry, Female, Fetus, Gestational Age, Head, Humans, Pregnancy, Prospective Studies, Reproducibility of Results, Ultrasonography, Prenatal, Imaging, Three-Dimensional
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