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Achieving Accurate Estimates Of Fetal Gestational Age And Personalised Predictions Of Fetal Growth Based On Data From An International Prospective Cohort Study: A Population-Based Machine Learning Study, Russell Fung, Jose Villar, Ali Dashti, Leila Cheikh Ismail, Eleonora Staines-Uria, Eric O. Ohuma, Maria Carvalho, Shama Munim, Zulfiqar Ahmed Bhutta, International Fetal And Newborn Growth Consortium For The 21st Century (Intergrowth-21st)
Achieving Accurate Estimates Of Fetal Gestational Age And Personalised Predictions Of Fetal Growth Based On Data From An International Prospective Cohort Study: A Population-Based Machine Learning Study, Russell Fung, Jose Villar, Ali Dashti, Leila Cheikh Ismail, Eleonora Staines-Uria, Eric O. Ohuma, Maria Carvalho, Shama Munim, Zulfiqar Ahmed Bhutta, International Fetal And Newborn Growth Consortium For The 21st Century (Intergrowth-21st)
Department of Obstetrics & Gynaecology
Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth.
Methods: …