{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T01:16:16Z","timestamp":1788052576006,"version":"build-2784847793"},"reference-count":52,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["10070022010001"],"award-info":[{"award-number":["10070022010001"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Obstet Gynecol Scand"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study aims to investigate non\u2010invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and methods<\/jats:title>\n                    <jats:p>An artificial neural network was trained for the identification of CHD using non\u2010invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5\u2009min) or more. A small negative trend between gestational age and accuracy was found.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed method combining recent advances in obtaining non\u2010invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography\u2010based screening complementary to the standard ultrasound\u2010based screening. More research is required to improve performance and determine the benefits to clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aogs.14623","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T01:55:43Z","timestamp":1691718943000},"page":"1511-1520","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease"],"prefix":"10.1111","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-4220","authenticated-orcid":false,"given":"Ivar R.","family":"de Vries","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology M\u00e1xima Medical Center  Veldhoven The Netherlands"},{"name":"Department of Electrical Engineering Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-3267","authenticated-orcid":false,"given":"Judith O. 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