{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2(7)"],"submitter":["Fung R"],"funding":["National Institute for Health Research (NIHR)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is det"],"journal":["The Lancet. Digital health"],"pagination":["e368-e375"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7323599"],"repository":["biostudies-literature"],"pubmed_title":["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."],"pmcid":["PMC7323599"],"pubmed_authors":["Laister A","Paulsene W","Patel B","Bornemeier S","Mwakio S","Zaidi S","Uauy R","Salomon LJ","Rossi C","Yuan Y","Katz M","Carew R","Gilli G","Kunnawar N","Cosgrove C","McCormick K","Newton CR","Oas K","Baricco M","Signorile F","Wanyonyi S","Venkataraman M","Gilli P","Rocco DA","Sande J","Staines-Urias E","Muninzwa D","Taori V","Buckle M","Reade D","Miller R","Gravett MG","Al-Jabri H","Villar J","Andersen HF","Wiladphaingern S","Golding J","Knight CL","Stein A","Salam R","Wang JH","Sharps M","Zhang JJ","Shorten M","Khedikar V","Roseman F","Vinayak S","Monyepote B","Shembekar C","Jacinta N","Leston A","Olearo E","Kihara M","Stones W","Saboo K","Wu QQ","Capp A","Roseman S","Al-Aamri F","Lephoto T","Seale A","da Silveira MF","Burnham O","Mainwaring M","Yellappan D","Jimenez-Bustos JM","Owende MG","Deutsch G","Paul V","Aranzeta L","Purwar M","Kilonzo J","Malgas L","Al-Habsi F","Alija M","Wilkinson A","Giolito M","Giuliani F","Ibanez D","Mahorkar C","Fernandes M","Dhami J","Varalda A","Carrara VI","Lewis T","Farhi F","Nosten F","Kennedy S","Gu SH","Ioannou C","Oberto M","Al-Zadjali WK","Ash S","Choudhary A","Frederick IO","Liu H","Kemp B","Jaffer YA","McGready R","Al-Lawatiya H","Rothwell PM","Ismail LC","Sclowitz IK","Chamberlain P","Carvalho M","Frigerio M","Rajan V","Kizidio J","Victora C","Savini S","Ketkar M","He YP","Mitidieri A","Weatherall D","Jackson N","Barsosio H","Maggiora E","Di NP","Zhang Y","Noble JA","Albernaz E","Bhat BA","Deshmukh S","Finkton D","Juangco FR","Chumlea WC","Sanchez LM","Wu MH","Papageorghiou AT","Cheikh IL","International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st)","Fonseca S","Ngami V","Fung R","Shah J","Napolitano R","Alam D","de Leon E","Ochieng R","Ahmed I","Juodvirsiene L","Wulff K","Sorensen TK","Bertino E","Salomon L","Matijasevich A","Raza A","Pang RY","Gaglioti P","Dashti A","Pang R","Lloyd S","Abubakar A","Choudhary S","Blakey I","Langer A","Kisiang'ani C","Mkrtychyan V","Sarris I","Salim M","Ohuma EO","Hussein S","Norris SA","Carter AA","Dolk H","Shen YJ","Norris T","Norris S","Berkley J","Mwangudzah H","Tayade K","Mulik I","Batra M","Guman Y","Singh A","Victora CG","Rivera J","Somani A","Wilson D","Puglia F","Batiuk C","Al-Abri J","Olivera I","Zainab G","Dighe M","Soria-Frisch A","Algren H","Min AM","Bhutta ZA","Macauley S","Wang L","Musee N","Ourmazd A","Bhan MK","Garza C","Enquobahrie D","Pace C","Staines Urias E","Mota D","Waller S","Condon C","Barros FC","Eskenazi B","Rovelli I","Sohoni S","Bradman A","Lambert A","Domingues M","Min A","Knight HE","Al-Rashidiya B","Occhi L","de Wet T","Al-Abduwani J","Walusuna L","Abbott SE","Craik R","Todros T","Munim S","Cararra VI","Corra LA","Burton F","Hoch L","Pan Y","Lumbiganon P","Altman DG","Kennedy SH","Jaiswal S","Danelon D","Dongaonkar D","Acedo J","Cheikh Ismail L"],"additional_accession":[]},"is_claimable":false,"name":"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.","description":"<h4>Background</h4>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.<h4>Methods</h4>Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is det","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jul","modification":"2025-04-19T23:43:43.219Z","creation":"2020-07-04T07:23:05Z"},"accession":"S-EPMC7323599","cross_references":{"pubmed":["32617525"],"doi":["10.1016/S2589-7500(20)30131-X"]}}