{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T12:27:36Z","timestamp":1794486456095,"version":"4.9.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["eClinicalMedicine"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.eclinm.2025.103250","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T20:54:56Z","timestamp":1748638496000},"page":"103250","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":35,"special_numbering":"C","title":["Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis"],"prefix":"10.1016","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0526-6329","authenticated-orcid":false,"given":"Elena","family":"D'Alberti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Patey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-9116","authenticated-orcid":false,"given":"Carolyn","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0921-1780","authenticated-orcid":false,"given":"Bojana","family":"\u0160alovi\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3603-4806","authenticated-orcid":false,"given":"Netzahualcoyotl","family":"Hernandez-Cruz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Alison","family":"Noble","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aris T.","family":"Papageorghiou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eclinm.2025.103250_bib1","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1016\/S0140-6736(24)00769-4","article-title":"Findings from the global burden of disease study 2021","volume":"403","author":"Murray","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.eclinm.2025.103250_bib4","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1136\/jech.54.9.660","article-title":"Infant mortality and congenital anomalies from 1950 to 1994: an international perspective","volume":"54","author":"Rosano","year":"2000","journal-title":"J Epidemiol Community Health"},{"key":"10.1016\/j.eclinm.2025.103250_bib5","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1002\/uog.14882","article-title":"Prenatal diagnosis of critical congenital heart disease reduces risk of death from cardiovascular compromise prior to planned neonatal cardiac surgery:a meta-analysis","volume":"45","author":"Holland","year":"2015","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib6","series-title":"WHO antenatal care recommendations for a positive pregnancy experience. Maternal and fetal assessment update: imaging ultrasound before 24 weeks of pregnancy","year":"2022"},{"key":"10.1016\/j.eclinm.2025.103250_bib7","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1002\/uog.26224","article-title":"ISUOG Practice Guidelines (updated): fetal cardiac screening","volume":"61","author":"Carvalho","year":"2023","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib8","first-page":"E20","article-title":"AIUM practice parameter for the performance of standard diagnostic obstetric ultrasound","volume":"43","year":"2024","journal-title":"J Ultrasound Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib9","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.123.031184","article-title":"Predicting high-risk fetal cardiac disease anticipated to need immediate postnatal stabilization and intervention with planned pediatric cardiac operating room delivery","volume":"13","author":"Moray","year":"2024","journal-title":"J Am Heart Assoc"},{"key":"10.1016\/j.eclinm.2025.103250_bib10","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1002\/pd.6617","article-title":"Delivery room and early postnatal management of neonates with congenital heart disease","volume":"44","author":"Ali","year":"2024","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.eclinm.2025.103250_bib11","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1002\/ijgo.12373","article-title":"Systematic review and meta-analysis of the performance of second-trimester screening for prenatal detection of congenital heart defects","volume":"140","author":"van Velzen","year":"2018","journal-title":"Int J Gynaecol Obstet"},{"key":"10.1016\/j.eclinm.2025.103250_bib12","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1002\/uog.20358","article-title":"Why are congenital heart defects being missed?","volume":"55","author":"van Nisselrooij","year":"2020","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib13","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/chd.12648","article-title":"Prenatal detection of critical cardiac outflow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines","volume":"13","author":"Sun","year":"2018","journal-title":"Congenit Heart Dis"},{"key":"10.1016\/j.eclinm.2025.103250_bib14","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000002158","article-title":"Diagnostic value of fetal echocardiography for congenital heart disease: a systematic review and meta-analysis","volume":"94","author":"Zhang","year":"2015","journal-title":"Medicine (Baltimore)"},{"key":"10.1016\/j.eclinm.2025.103250_bib15","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1002\/uog.22048","article-title":"Cost-effectiveness of prenatal screening methods for congenital heart defects in pregnancies conceived by in-vitro fertilization","volume":"57","author":"Chung","year":"2021","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib16","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1002\/uog.21977","article-title":"Detection of fetal cardiac anomalies: cost-effectiveness of increased number of cardiac views","volume":"55","author":"Bak","year":"2020","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib17","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.echo.2023.12.013","article-title":"Advances in the application of artificial intelligence in fetal echocardiography","volume":"37","author":"Zhang","year":"2024","journal-title":"J Am Soc Echocardiogr"},{"key":"10.1016\/j.eclinm.2025.103250_bib18","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1515\/jpm-2023-0052","article-title":"Evaluation of an artificial intelligent algorithm (Heartassist\u2122) to automatically assess the quality of second trimester cardiac views: a prospective study","volume":"51","author":"Pietrolucci","year":"2023","journal-title":"J Perinat Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib19","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.eclinm.2025.103250_bib20","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement","volume":"319","author":"McInnes","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.eclinm.2025.103250_bib21","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1001\/jamasurg.2021.0522","article-title":"MOOSE reporting guidelines for meta-analyses of observational studies","volume":"156","author":"Brooke","year":"2021","journal-title":"JAMA Surg"},{"key":"10.1016\/j.eclinm.2025.103250_bib22","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.1016\/j.eclinm.2025.103250_bib23","series-title":"World development indicators. World Bank Country and Lending Groups","year":"2024"},{"issue":"1","key":"10.1016\/j.eclinm.2025.103250_bib24","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1186\/s12874-021-01381-z","article-title":"Conducting proportional meta-analysis in different types of systematic reviews: a guide for synthesisers of evidence","volume":"21","author":"Barker","year":"2021","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.eclinm.2025.103250_bib25","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib26","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.eclinm.2025.103250_bib27","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TMI.2019.2946059","article-title":"Fetal congenital heart disease echocardiogram screening based on DGACNN: adversarial one-class classification combined with video transfer learning","volume":"39","author":"Gong","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.eclinm.2025.103250_bib28","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/s41591-021-01342-5","article-title":"An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease","volume":"27","author":"Arnaout","year":"2021","journal-title":"Nat Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib29","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3390\/app11010371","article-title":"Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning","volume":"11","author":"Komatsu","year":"2021","journal-title":"Appl Sci"},{"key":"10.1016\/j.eclinm.2025.103250_bib30","doi-asserted-by":"crossref","first-page":"6454","DOI":"10.3390\/jcm11216454","article-title":"Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases","volume":"11","author":"Nurmaini","year":"2022","journal-title":"J Clin Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib31","doi-asserted-by":"crossref","first-page":"551","DOI":"10.3390\/biomedicines10030551","article-title":"Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening","volume":"10","author":"Sakai","year":"2022","journal-title":"Biomedicines"},{"key":"10.1016\/j.eclinm.2025.103250_bib32","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1002\/pd.6220","article-title":"Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence","volume":"42","author":"Wang","year":"2022","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.eclinm.2025.103250_bib33","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s10554-022-02566-3","article-title":"Application of machine learning in screening for congenital heart diseases using fetal echocardiography","volume":"38","author":"Truong","year":"2022","journal-title":"Int J Cardiovasc Imaging"},{"key":"10.1016\/j.eclinm.2025.103250_bib34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1002\/uog.27503","article-title":"Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection","volume":"63","author":"Athalye","year":"2024","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib35","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41746-023-00883-y","article-title":"A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography","volume":"6","author":"Tang","year":"2023","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib36","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1002\/pd.6445","article-title":"Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: how does performance compare to a current screening programme?","volume":"44","author":"Day","year":"2024","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.eclinm.2025.103250_bib37","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1515\/jpm-2023-0041","article-title":"Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning","volume":"51","author":"Yang","year":"2023","journal-title":"J Perinat Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1002\/uog.27577","article-title":"Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?","volume":"64","author":"Day","year":"2024","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103229","article-title":"Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound","volume":"97","author":"Yang","year":"2024","journal-title":"Med Image Anal"},{"key":"10.1016\/j.eclinm.2025.103250_bib40","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1002\/uog.27608","article-title":"Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta","volume":"64","author":"Taks\u00f8e-Vester","year":"2024","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib41","doi-asserted-by":"crossref","first-page":"26","DOI":"10.21037\/tp-23-394","article-title":"Application of neural networks in prenatal diagnosis of atrioventricular septal defect","volume":"13","author":"Zhou","year":"2024","journal-title":"Transl Pediatr"},{"key":"10.1016\/j.eclinm.2025.103250_bib42","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.eclinm.2025.103250_bib43","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","article-title":"SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound","volume":"36","author":"Baumgartner","year":"2017","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.eclinm.2025.103250_bib44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1002\/uog.27886","article-title":"OP02.04: automated segmentation of fetal heart three-vessel view ultrasound video clips to facilitate prenatal assessment of congenital heart defects","volume":"64","author":"Hernandez-Cruz","year":"2024","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib45","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/uog.23740","article-title":"First-trimester ultrasound detection of fetal heart anomalies: systematic review and meta-analysis","volume":"59","author":"Karim","year":"2022","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib46","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1002\/pd.4753","article-title":"Utility of novel fetal echocardiographic morphometric measures of the aortic arch in the diagnosis of neonatal coarctation of the aorta","volume":"36","author":"Arya","year":"2016","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.eclinm.2025.103250_bib47","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1002\/uog.23558","article-title":"Fetal echocardiographic predictors of biventricular circulation in hypoplastic left heart complex","volume":"58","author":"Haberer","year":"2021","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib48","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1002\/uog.27576","article-title":"Diagnostic accuracy of prenatal ultrasound in coarctation of aorta: systematic review and individual participant data meta-analysis","volume":"63","author":"Villala\u00edn","year":"2024","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10.1016\/j.eclinm.2025.103250_bib49","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1002\/pd.6287","article-title":"RE: Wang et al. Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence","volume":"43","author":"Day","year":"2023","journal-title":"Prenat Diagn"},{"key":"10.1016\/j.eclinm.2025.103250_bib50","article-title":"Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration","volume":"380","author":"Debray","year":"2023","journal-title":"BMJ"}],"container-title":["eClinicalMedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589537025001828?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589537025001828?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:52:43Z","timestamp":1760215963000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2589537025001828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":48,"alternative-id":["S2589537025001828"],"URL":"https:\/\/doi.org\/10.1016\/j.eclinm.2025.103250","relation":{},"ISSN":["2589-5370"],"issn-type":[{"value":"2589-5370","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis","name":"articletitle","label":"Article Title"},{"value":"eClinicalMedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eclinm.2025.103250","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"103250"}}