The DOI matches its Crossref record.
An intelligent diagnostic system for the assessment of gestational age based on ultrasonic fetal head measurements
Abstract
The object of this study is the assessment of gestational age by using fetal biparietal diameter and head circumference based on automated image analysis and artificial neural networks. Standard ultrasonic measurements were made in 143 normal fetuses between 14 and 40 weeks' gestation. Six hundred and thirteen fetal head images were transferred to a microcomputer environment by means of a frame grabber and spatial software. Biparietal diameter and head circumference measurements were made by automated image processing and analysis techniques. In the next stage, these two fetal parameters were used to determine the gestational age by using an unsupervised artificial neural network. Back propagation learning algorithm was trained by 552 fetal head images and the system was tested with the remaining 61 images. It has been demonstrated that 98% of gestational weeks were estimated correctly by our system.
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In the citing paper
“starting in 1996 with a pioneering study of Beksaç et al. on the estimation of GA via the calculation of the fetal biparietal diameter and HC [146].”
In the citing paper
“or very late stages of pregnancy [146,147,152], the latter of which was due to the thickened texture of the fetal skull.”
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Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44
Data mining for preterm birth prediction
Goodwin, L., & Maher, S. M. (2000) · doi:10.1145/335603.335680 · Cited by 26
Fetal biometry: a comparison between experienced sonographers and automated measurements
Žalud, I., Good, S., Carneiro, G., Georgescu, B., Aoki, K., Green, L., Shahrestani, F., & Okumura, R. (2009) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767050802415736 · Cited by 23
“The other studies confirmed feasibility of automatic fetal biometry [14,15].”
BackgroundComputerized prediction system for the route of delivery (vaginal birth versus cesarean section)
Beksaç, M. S., Tanaçan, A., Bacak, H. O., & Leblebi̇ci̇oğlu, K. (2018) · Journal of Perinatal Medicine · doi:10.1515/jpm-2018-0022 · Cited by 21
Self-citationUse of artificial intelligence for gestational age estimation: a systematic review and meta-analysis
Naz, S., Noorani, S., Zaidi, S. A. J., Rahman, A. R., Sattar, S., Das, J. K., & Hoodbhoy, Z. A. (2025) · Frontiers in Global Women s Health · doi:10.3389/fgwh.2025.1447579 · Cited by 11
Metadata
- Authors
- Beksaç, M. S., Odçık ın, Z., Egemena, A., & Karakaş, Ü.
- Affiliations
- Hacettepe University (TR)
- Department of Computer Sciences & Engineering of Hacettepe University, Beytepe, Ankara, Turkey
- Corresponding author
- Beksaç, M. Sinan
- Venue
- Technology and Health Care
- Publisher
- SAGE Publications
- Published
- 1996-08-01
- Volume
- 4
- Issue
- 2
- Pages
- 223-231
- ISSN
- 0928-7329 (ISSN-L)
- 1878-7401
- OpenAlex
- W4297833024
- Cited by
- 11
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 1 · 2019: 1 · 2018: 2 · 2013: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Technology and Health Care (publishedVersion)
- PubMed (publishedVersion)
- PubMed
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- gestational age estimation, fetal biparietal diameter, fetal head circumference, automated image analysis, unsupervised neural networks, backpropagation, diagnostic systems, prenatal diagnosis
- MeSH terms
- Algorithms, Cephalometry, Female, Gestational Age, Humans, Image Interpretation, Computer-Assisted, Microcomputers, Pregnancy, Software, Reproducibility of Results, Ultrasonography, Prenatal, Neural Networks, Computer
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