{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:06:36Z","timestamp":1787169996215,"version":"build-2736575974"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>For precise diagnosis and effective management of atrial septal defects, it is of utmost significance to conduct elementary screenings on children. The primary aim of this study is to develop and authenticate an objective methodology for detecting atrial septal defects by employing deep learning (DL) on chest x-ray (CXR) examinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study encompassed echocardiographs and corresponding Chest x-rays that were consistently gathered at Qingdao Women's and Children's Hospital from 2018 to 2022. Based on a collaborative diagnosis report by two cardiologists with over 10 years of experience in echocardiography, these radiographs were classified as positive or negative for atrial septal defect, and then divided into training and validation datasets. An artificial intelligence model was formulated by utilizing the training dataset and fine-tuned using the validation dataset. To evaluate the efficacy of the model, an assessment of the area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value was conducted employing the validation dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This research encompassed a total of 420 images from individuals. The screening accuracy and recall rate of the model surpass 90%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>One of profound neural network models predicated on chest x-ray radiographs (a traditional, extensively employed, and economically viable examination) proves highly advantageous in the assessment for atrial septal defect.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2023.1203933","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T01:40:05Z","timestamp":1694396405000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["The development and validation of an artificial intelligence-based screening method for atrial septal defect in children's chest x-rays"],"prefix":"10.3389","volume":"11","author":[{"given":"Li","family":"Zhixin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luo","family":"Gang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhixian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Silin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1093\/eurheartj\/ehab646","article-title":"Atrial septal defect in adulthood: a new paradigm for congenital heart disease","volume":"43","author":"Brida","year":"2022","journal-title":"Eur Heart J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ccl.2020.04.001","article-title":"Atrial septal defect","volume":"38","author":"Bradley","year":"2020","journal-title":"Cardiol Clin"},{"key":"B3","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1159\/000496348","article-title":"Atrial septal defect sizing and transcatheter closure","volume":"142","author":"Boudoulas","year":"2019","journal-title":"Cardiology"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1016\/S0140-6736(13)62145-5","article-title":"Atrial septal defects","volume":"383","author":"Geva","year":"2014","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jjcc.2014.09.002","article-title":"Current concept of transcatheter closure of atrial septal defect in adults","volume":"65","author":"Akagi","year":"2015","journal-title":"J Cardiol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.jcin.2021.01.008","article-title":"Atrial septal defect: transcatheter closure is not bad, but there is more to the story","volume":"14","author":"Latson","year":"2021","journal-title":"JACC Cardiovasc Interv"},{"key":"B7","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1111\/chd.12298","article-title":"Long-term follow-up of \u201csimple\u201d lesions\u2013atrial septal defect, ventricular septal defect, and coarctation of the aorta","volume":"10","author":"Goldberg","year":"2015","journal-title":"Congenit Heart Dis"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","article-title":"2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) with the special contribution of the Heart Failure Association (HFA) of the ESC","volume":"42","author":"McDonagh","year":"2021","journal-title":"Eur Heart J"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1093\/eurheartj\/ehs104","article-title":"ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the task force for the diagnosis and treatment of acute and chronic heart failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC","volume":"33","author":"McMurray","year":"2012","journal-title":"Eur Heart J"},{"key":"B10","doi-asserted-by":"publisher","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.3348\/kjr.2020.0536","article-title":"Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for COVID-19","volume":"21","author":"Hwang","year":"2020","journal-title":"Korean J Radiol."},{"key":"B12","author":"Fey","year":""},{"key":"B13","doi-asserted-by":"publisher","first-page":"3408","DOI":"10.1021\/acs.jcim.0c00451","article-title":"TorchANI: a free and open source PyTorch-based deep learning implementation of the ANI neural network potentials","volume":"60","author":"Gao","year":"2020","journal-title":"J Chem Inf Model"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.3390\/s23031450","article-title":"Towards generating realistic wrist pulse signals using enhanced one dimensional wasserstein GAN","volume":"23","author":"Chang","year":"2023","journal-title":"Sensors"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/tmi.2021.3078370","article-title":"Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos","volume":"40","author":"Chen","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"B16","first-page":"6649","author":"Kim","year":"2020"},{"key":"B17","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1093\/ehjdh\/ztab102","article-title":"Artificial intelligence-based detection of aortic stenosis from chest radiographs","volume":"3","author":"Ueda","year":"2022","journal-title":"Eur Heart J Digit Health"},{"key":"B18","doi-asserted-by":"publisher","first-page":"5890","DOI":"10.1007\/s00330-022-08752-0","article-title":"Artificial intelligence-based detection of atrial fibrillation from chest radiographs","volume":"32","author":"Matsumoto","year":"2022","journal-title":"Eur Radiol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11036-022-02021-6","article-title":"Numerical grad-cam based explainable convolutional neural network for brain tumor diagnosis","author":"Marmolejo","year":"2022","journal-title":"Mob Netw Appl"},{"key":"B20","first-page":"175","author":"Borg","year":"2021"},{"key":"B21","first-page":"839","author":"Chattopadhay","year":"2018"}],"container-title":["Frontiers in Pediatrics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2023.1203933\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T01:40:06Z","timestamp":1694396406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2023.1203933\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"references-count":21,"alternative-id":["10.3389\/fped.2023.1203933"],"URL":"https:\/\/doi.org\/10.3389\/fped.2023.1203933","relation":{},"ISSN":["2296-2360"],"issn-type":[{"value":"2296-2360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,11]]},"article-number":"1203933"}}