{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:49:29Z","timestamp":1790113769987,"version":"4.0.1"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Fonden","doi-asserted-by":"publisher","award":["NNFSA170030576"],"award-info":[{"award-number":["NNFSA170030576"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp; Gyne"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18\u201322\u2010week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2\u2009days. Cardiac biometrics obtained from the four\u2010chamber and three\u2010vessel views were included in a logistic regression\u2010based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver\u2010operating\u2010characteristics (ROC) curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      At the 18\u201322\u2010week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV\/LV areas and main pulmonary artery\/ascending aorta diameters showed significant differences, with\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200973) and healthy controls (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The integration of AI technology with automated cardiac biometric measurements obtained during the 18\u201322\u2010week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. \u00a9 2024 The Authors.\n                      <jats:italic>Ultrasound in Obstetrics &amp; Gynecology<\/jats:italic>\n                      published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/uog.27608","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T01:19:36Z","timestamp":1707527976000},"page":"36-43","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Role of artificial\u2010intelligence\u2010assisted automated cardiac biometrics in prenatal screening for coarctation of aorta"],"prefix":"10.1002","volume":"64","author":[{"given":"C. A.","family":"Taks\u00f8e\u2010Vester","sequence":"first","affiliation":[{"name":"Department of Clinical Medicine, Faculty of Health and Medical Sciences University of Copenhagen  Copenhagen Denmark"},{"name":"Center of Fetal Medicine, Department of Gynecology, Fertility and Obstetrics Copenhagen University Hospital, Rigshospitalet  Copenhagen Denmark"},{"name":"Copenhagen Academy of Medical Education and Simulation (CAMES) Rigshospitalet  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Mikolaj","sequence":"additional","affiliation":[{"name":"Technical University of Denmark  Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"O. B. B.","family":"Petersen","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, Faculty of Health and Medical Sciences University of Copenhagen  Copenhagen Denmark"},{"name":"Center of Fetal Medicine, Department of Gynecology, Fertility and Obstetrics Copenhagen University Hospital, Rigshospitalet  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. G.","family":"Vejlstrup","sequence":"additional","affiliation":[{"name":"Department of Cardiology Copenhagen University Hospital, Rigshospitalet  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. N.","family":"Christensen","sequence":"additional","affiliation":[{"name":"Technical University of Denmark  Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Feragen","sequence":"additional","affiliation":[{"name":"Technical University of Denmark  Lyngby Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Copenhagen  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. B. S.","family":"Svendsen","sequence":"additional","affiliation":[{"name":"Copenhagen Academy of Medical Education and Simulation (CAMES) Rigshospitalet  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-5564","authenticated-orcid":false,"given":"M. G.","family":"Tolsgaard","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, Faculty of Health and Medical Sciences University of Copenhagen  Copenhagen Denmark"},{"name":"Center of Fetal Medicine, Department of Gynecology, Fertility and Obstetrics Copenhagen University Hospital, Rigshospitalet  Copenhagen Denmark"},{"name":"Copenhagen Academy of Medical Education and Simulation (CAMES) Rigshospitalet  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.179918"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.6291"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.4753"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.2804"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.4211"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2016.06.008"},{"key":"e_1_2_8_8_1","volume-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"Budd S","year":"2021"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60334-2_24"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01342-5"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3034367"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.22167"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00246-021-02620-2"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/echo.13140"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12265-022-10335-9"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10163690"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.116.024068"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.7863\/ultra.15.06049"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00246-020-02509-6"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S99477"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1159\/000494838"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.108.787598"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.8961"},{"key":"e_1_2_8_26_1","first-page":"2825","article-title":"Scikit\u2010learn: machine learning in Python","volume":"12","author":"Pedregosa F","year":"2011","journal-title":"J Mach Learn Res"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2020.1849109"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.4452"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.23546"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29490-3"}],"updated-by":[{"DOI":"10.1002\/uog.29156","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T00:00:00Z","timestamp":1736035200000}}],"container-title":["Ultrasound in Obstetrics &amp; Gynecology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/uog.27608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T14:20:21Z","timestamp":1735568421000},"score":1,"resource":{"primary":{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/10.1002\/uog.27608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10.1002\/uog.27608"],"URL":"https:\/\/doi.org\/10.1002\/uog.27608","archive":["Portico"],"relation":{},"ISSN":["0960-7692","1469-0705"],"issn-type":[{"value":"0960-7692","type":"print"},{"value":"1469-0705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2023-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}