{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T21:29:31Z","timestamp":1791062971333,"version":"4.1.0"},"reference-count":34,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Ventricular septal defect (VSD) is the most common congenital heart defect (CHD), affecting 2\u20136\/1000 live births and representing nearly 30% of pediatric CHD cases. Despite advances in imaging, disparities in diagnosis persist in low-resource settings. Early detection and management are essential to prevent complications and improve outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To systematically evaluate deep learning (DL) applications for screening, diagnosis, and management of VSD in pediatric populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A systematic MEDLINE\/PubMed search was conducted in January 2025 for studies published between 2000 and 2025 using terms including \u201cdeep learning,\u201d \u201cartificial intelligence,\u201d and \u201cventricular septal defect.\u201d Studies were included if they were original, peer-reviewed, in English, involved pediatric populations, and reported on DL applications in VSD care. Two authors independently screened and reviewed studies. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Fourteen studies met the inclusion criteria, encompassing 1,434,840 subjects. Due to heterogeneity, no meta-analysis was performed. Convolutional neural networks (CNNs) applied to prenatal echocardiography reached up to 100% sensitivity and 98% accuracy for VSD detection. DL models analyzing phonocardiograms showed &gt;90% accuracy, comparable to expert auscultation. Chest X-ray-based CNNs differentiated between atrial and VSDs with an area under the curve (AUC) of 0.91. Risk stratification models predicted spontaneous VSD closure (AUC 0.98) and pulmonary hypertension risk (AUC 0.81).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>DL models, especially CNNs, show promise for enhancing VSD diagnosis and management in children. However, most studies are retrospective, lack external validation, and use inconsistent outcomes. Broader clinical integration requires prospective, multicenter trials and improved interpretability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/apc.apc_163_25","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T07:00:28Z","timestamp":1776236428000},"page":"53-61","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["A systematic review on deep learning in ventricular septal defect screening, diagnosis, and management"],"prefix":"10.4103","volume":"19","author":[{"given":"Ofir","family":"Brem","sequence":"first","affiliation":[{"name":"Department of Pediatrics, David Geffen School of Medicine at UCLA, UCLA Mattel Children\u2019s Hospital, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nili Schamroth","family":"Pravda","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Adult Congenital Heart Disease Service, Tel Aviv Medical Center, Tel Aviv, Israel"},{"name":"Gray Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Xu","sequence":"additional","affiliation":[{"name":"Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA"},{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA"},{"name":"Department of Bioengineering, University of California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim-Lien","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA"},{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA"},{"name":"Department of Bioengineering, University of California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majid","family":"Husain","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Division of Pediatric Cardiology, UCLA Mattel Children\u2019s Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"R2-20260420","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1186\/s12872-020-01781-x","article-title":"Global prevalence of congenital heart disease in school-age children: A meta-analysis and systematic review","volume":"20","author":"Liu","year":"2020","journal-title":"BMC Cardiovasc Disord"},{"key":"R3-20260420","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1111\/chd.12528","article-title":"Incidence and natural history of neonatal isolated ventricular septal defects: Do we know everything? 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