Deep learning fetal ultrasound video model match human observers in biometric measurements
Open accessAbstract
Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated. Main results. We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model. Significance. We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.
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Research works printed but not linked: 2
- Bano S, Dromey B, Vasconcelos F, Napolitano R, David A L, Peebles D M and Stoyanov D 2021 AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes Medical Image Computing and Computer Assisted Intervention-MICCAI 2021 ed M de Bruijne et al vol 12907 (Cham: Springer International Publishing) pp 228-38
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BackgroundMethodDevelopment of a Machine Learning Model for Sonographic Assessment of Gestational Age
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Self-citationFetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning
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- Authors
- Płotka, S., Klasa, A., Lisowska, A., Seliga-Siwecka, J., Lipa, M., Trzciński, T., & Sitek, A.
- Affiliations
- Warsaw University of Technology (PL)
- Fetai Health Ltd.
- Sano – Centre for Computational Personalised Medicine International Research Foundation (PL)
- Medical University of Warsaw (PL)
- Warsaw University of Technology
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2022-02-16
- Volume
- 67
- Issue
- 4
- Pages
- 045013
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W4205908798
- Cited by
- 57
- References
- 49
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 6 · 2023: 15 · 2022: 7
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (857533)
- Fundacja na rzecz Nauki Polskiej (IRAP)
- 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 › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- prenatal ultrasound, automated measurement, gestational age estimation, fetal weight estimation, head circumference, biparietal diameter, abdominal circumference, femur length, multi-task convolutional neural network, spatiotemporal feature extraction, observer variability
- MeSH terms
- Deep Learning, Biometry, Female, Fetus, Gestational Age, Humans, Pregnancy, Ultrasonography, Prenatal
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