Lopian, M; Ulusoy, CO; Prasad, S; Segal, E; Khalil, A
(2025)
Accurate prediction of growth-restricted neonates at term using machine learning.
Am J Obstet Gynecol.
ISSN 1097-6868
https://doi.org/10.1016/j.ajog.2025.01.024
SGUL Authors: Khalil, Asma
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Official URL: https://doi.org/10.1016/j.ajog.2025.01.024
| Item Type: | Article |
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| Additional Information: | © 2025 The Author(s). Published by Elsevier Inc. Under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | Doppler, adverse perinatal, artificial intelligence, cerebroplacental ratio, estimated fetal weight, fetal biometry, growth restriction, machine learning, outcomes, small-for-gestational-age, third-trimester ultrasound scan, uterine artery, 1114 Paediatrics and Reproductive Medicine, Obstetrics & Reproductive Medicine |
| SGUL Research Institute / Research Centre: | Academic Structure > Cardiovascular & Genomics Research Institute Academic Structure > Cardiovascular & Genomics Research Institute > Vascular Biology |
| Journal or Publication Title: | Am J Obstet Gynecol |
| ISSN: | 1097-6868 |
| Language: | eng |
| Publisher License: | Creative Commons: Attribution 4.0 |
| PubMed ID: | 39864484 |
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| URI: | https://openaccess.sgul.ac.uk/id/eprint/117125 |
| Publisher's version: | https://doi.org/10.1016/j.ajog.2025.01.024 |
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