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, 232 (5). e170-e173. ISSN 1097-6868 https://doi.org/10.1016/j.ajog.2025.01.024
SGUL Authors: Khalil, Asma

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Item Type:	Article
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
Dates:	Date Event 2025-05-08 Published 2025-01-24 Published Online 2025-01-15 Accepted
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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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