{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:14:49Z","timestamp":1790399689881,"version":"4.1.0"},"reference-count":24,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universitas Sriwijaya","award":["Proffesional Grants 2022"],"award-info":[{"award-number":["Proffesional Grants 2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Early prenatal screening with an ultrasound (US) can significantly lower newborn mortality caused by congenital heart diseases (CHDs). However, the need for expertise in fetal cardiologists and the high volume of screening cases limit the practically achievable detection rates. Hence, automated prenatal screening to support clinicians is desirable. This paper presents and analyses potential deep learning (DL) techniques to diagnose CHDs in fetal USs. Four convolutional neural network architectures were compared to select the best classifier with satisfactory results. Hence, dense convolutional network (DenseNet) 201 architecture was selected for the classification of seven CHDs, such as ventricular septal defect, atrial septal defect, atrioventricular septal defect, Ebstein\u2019s anomaly, tetralogy of Fallot, transposition of great arteries, hypoplastic left heart syndrome, and a normal control. The sensitivity, specificity, and accuracy of the DenseNet201 model were 100%, 100%, and 100%, respectively, for the intra-patient scenario and 99%, 97%, and 98%, respectively, for the inter-patient scenario. We used the intra-patient DL prediction model to validate our proposed model against the prediction results of three expert fetal cardiologists. The proposed model produces a satisfactory result, which means that our model can support expert fetal cardiologists to interpret the decision to improve CHD diagnostics. This work represents a step toward the goal of assisting front-line sonographers with CHD diagnoses at the population level.<\/jats:p>","DOI":"10.3390\/jcm11216454","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T06:42:17Z","timestamp":1667371337000},"page":"6454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2952","authenticated-orcid":false,"given":"Siti","family":"Nurmaini","sequence":"first","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radiyati Umi","family":"Partan","sequence":"additional","affiliation":[{"name":"Internal Medicine, Mohammad Hoesin General Hospital, Palembang 30126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5778-0074","authenticated-orcid":false,"given":"Nuswil","family":"Bernolian","sequence":"additional","affiliation":[{"name":"Division of Fetomaternal, Department of Obstetrics and Gynaecology, Mohammad Hoesin General Hospital, Palembang 30126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-5717","authenticated-orcid":false,"given":"Annisa","family":"Darmawahyuni","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-3486","authenticated-orcid":false,"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanes C.","family":"Mose","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology, Faculty of Medicine, Padjajaran University, Bandung 45363, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100e115","DOI":"10.4172\/2167-0897.100e115","article-title":"Fetal anomaly screening for detection of congenital heart defects","volume":"5","author":"Singh","year":"2016","journal-title":"J. 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