{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:16:23Z","timestamp":1778516183890,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Hainan Province Science and Technology Special Fund","award":["ZDYF2024SHFZ059"],"award-info":[{"award-number":["ZDYF2024SHFZ059"]}]},{"name":"Sanya Science and Technology Special Fund","award":["No. 2022KJCX41"],"award-info":[{"award-number":["No. 2022KJCX41"]}]},{"name":"Science and Technology Committee of Shanghai","award":["No. 20DZ2253900"],"award-info":[{"award-number":["No. 20DZ2253900"]}]},{"name":"Shenzhen Medical Research Special Project clinical multi-center study","award":["No. C2405001"],"award-info":[{"award-number":["No. C2405001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-01916-0","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:05:03Z","timestamp":1778515503000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection of Patent Ductus Arteriosus in Pediatric Patients Using Doppler Ultrasonography Videos Based on a Transformer Model"],"prefix":"10.1007","author":[{"given":"Wenjing","family":"Hong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanping","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xike","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linghui","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiman","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxue","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianze","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hansong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liebin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6182-7780","authenticated-orcid":false,"given":"Yuqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"1916_CR1","doi-asserted-by":"crossref","unstructured":"Elsheikh RG, Darweish AZ, Elsetiha M, Kamel H: Comparative study between real time three dimensional echocardiogram and angiography in evaluation of patent ductus arteriosus, single center experience. J Saudi Heart Assoc 26:204\u2013211, 2014","DOI":"10.1016\/j.jsha.2014.02.005"},{"key":"1916_CR2","doi-asserted-by":"crossref","unstructured":"de Boode WP, Kluckow M, McNamara PJ, Gupta S: Role of neonatologist-performed echocardiography in the assessment and management of patent ductus arteriosus physiology in the newborn. Semin Fetal Neonatal Med 23:292\u2013297, 2018","DOI":"10.1016\/j.siny.2018.03.007"},{"key":"1916_CR3","doi-asserted-by":"crossref","unstructured":"Terrin G, Di Chiara M, Boscarino G, Versacci P, Di Donato V, Giancotti A, Pacelli E, Faccioli F, Onest\u00e0 E, Corso C, Ticchiarelli A, De Curtis M: Echocardiography-guided management of preterms with patent ductus arteriosus influences the outcome: A cohort study. Front Pediatr 8:582735, 2020","DOI":"10.3389\/fped.2020.582735"},{"key":"1916_CR4","doi-asserted-by":"crossref","unstructured":"Jiang H, Tang Q, Jiang Y, Li N, Tang X, Xia H: Echocardiographic and pathomorphological features in fetuses with ductal\u2010dependent congenital heart diseases. Echocardiography 36:1736\u20131743, 2019","DOI":"10.1111\/echo.14452"},{"key":"1916_CR5","doi-asserted-by":"crossref","unstructured":"Liu B, Chang H, Yang D, Yang F, Wang Q, Deng Y, Li L, Lv W, Zhang B, Yu L, Burkhoff D, He K: A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci Rep 13:3, 2023","DOI":"10.1038\/s41598-022-27211-w"},{"key":"1916_CR6","doi-asserted-by":"crossref","unstructured":"Park S, Moon J, Eun H, Hong J-H, Lee K: Artificial intelligence-based diagnostic support system for patent ductus arteriosus in premature infants. J Clin Med 13:2089, 2024","DOI":"10.3390\/jcm13072089"},{"key":"1916_CR7","doi-asserted-by":"crossref","unstructured":"Lei H, Ashrafi A, Chang P, Chang A, Lai W: Patent ductus arteriosus (PDA) detection in echocardiograms using deep learning. Intell-Based Med 6:100054, 2022","DOI":"10.1016\/j.ibmed.2022.100054"},{"key":"1916_CR8","doi-asserted-by":"crossref","unstructured":"Erno J, Gomes T, Baltimore C, Lineberger JP, Smith DH, Baker GH: Automated identification of patent ductus arteriosus using a computer vision model. J Ultrasound Med 42:2707\u20132713, 2023","DOI":"10.1002\/jum.16305"},{"key":"1916_CR9","doi-asserted-by":"crossref","unstructured":"Deepika S, Jaisankar N: Enhanced heart disease classification using dual attention mechanisms and 3D-echo fusion algorithm in echocardiogram videos. IEEE Access 13:1609\u20131620, 2025","DOI":"10.1109\/ACCESS.2024.3522996"},{"key":"1916_CR10","doi-asserted-by":"crossref","unstructured":"Wu L, Dong B, Liu X, Hong W, Chen L, Gao K, Sheng Q, Yu Y, Zhao L, Zhang Y: Standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation. Front Pediatr 9:770182, 2022","DOI":"10.3389\/fped.2021.770182"},{"key":"1916_CR11","doi-asserted-by":"crossref","unstructured":"Jiang X, Yu J, Ye J, Jia W, Xu W, Shu Q: A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography. World J Pediatr Surg 6:e000580, 2023","DOI":"10.1136\/wjps-2023-000580"},{"key":"1916_CR12","doi-asserted-by":"crossref","unstructured":"Hong W, Sheng Q, Dong B, Wu L, Chen L, Zhao L, Liu Y, Zhu J, Liu Y, Xie Y, Yu Y, Wang H, Yuan J, Ge T, Zhao L, Liu X, Zhang Y: Automatic detection of secundum atrial septal defect in children based on color doppler echocardiographic images using convolutional neural networks. Front Cardiovasc Med 9:834285, 2022","DOI":"10.3389\/fcvm.2022.834285"},{"key":"1916_CR13","doi-asserted-by":"crossref","unstructured":"Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, Forrest JK, McNamara RL, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz HM, Wang Z, Khera R: Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J 44:4592\u20134604, 2023","DOI":"10.1093\/eurheartj\/ehad456"},{"key":"1916_CR14","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Quintana S, Schwarz CE, Shelevytsky I, Shelevytska V, Semenova O, Factor A, Popovici E, Temko A: A framework for AI-assisted detection of patent ductus arteriosus from neonatal phonocardiogram. Health Care (Don Mills) 9:169, 2021","DOI":"10.3390\/healthcare9020169"},{"key":"1916_CR15","doi-asserted-by":"crossref","unstructured":"Zhu Y-C, Jin P-F, Bao J, Jiang Q, Wang X: Thyroid ultrasound image classification using a convolutional neural network. Ann Transl Med 9:1526, 2021","DOI":"10.21037\/atm-21-4328"},{"key":"1916_CR16","doi-asserted-by":"crossref","unstructured":"Nguyen MB, Villemain O, Friedberg MK, Lovstakken L, Rusin CG, Mertens L: Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes. Front Radiol 2:881777, 2022","DOI":"10.3389\/fradi.2022.881777"},{"key":"1916_CR17","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141 ukasz, Polosukhin I: Attention is all you need. Adv Neural Inf Process Syst 30:, 2017"},{"key":"1916_CR18","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N: An image is worth 16x16 words: Transformers for image recognition at scale. Int Conf Learn Represent ICLR 2021"},{"key":"1916_CR19","unstructured":"Bertasius G, Wang H, Torresani L: Is space-time attention all you need for video understanding? Proc 38th Int Conf Mach Learn 2:813\u2013824, 2021"},{"key":"1916_CR20","doi-asserted-by":"crossref","unstructured":"Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H: Video swin transformer. Proc IEEECVF Conf Comput Vis Pattern Recognit 3202\u20133211, 2022","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"1916_CR21","doi-asserted-by":"crossref","unstructured":"Neimark D, Bar O, Zohar M, Asselmann D: Video transformer network. 2021 IEEECVF Int Conf Comput Vis Workshop ICCVW 3156\u20133165, 2021","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"1916_CR22","doi-asserted-by":"crossref","unstructured":"Qiao S, Pang S, Sun Y, Luo G, Yin W, Zhao Y, Pan S, Lv Z: SPReCHD: Four-chamber semantic parsing network for recognizing fetal congenital heart disease in medical metaverse. IEEE J Biomed Health Inform 28:3672\u20133682, 2024","DOI":"10.1109\/JBHI.2022.3218577"},{"key":"1916_CR23","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A: Quo vadis, action recognition? A new model and the kinetics dataset. 2017 IEEE Conf Comput Vis Pattern Recognit CVPR 4724\u20134733, 2017","DOI":"10.1109\/CVPR.2017.502"},{"key":"1916_CR24","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C: X3D: Expanding architectures for efficient video recognition. 2020 IEEECVF Conf Comput Vis Pattern Recognit CVPR 200\u2013210, 2020","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"1916_CR25","doi-asserted-by":"crossref","unstructured":"Huang M, Qian H, Han Y, Xiang W: R(2+1)D-based two-stream CNN for human activities recognition in videos. 2021 40th Chin Control Conf CCC 7932\u20137937, 2021","DOI":"10.23919\/CCC52363.2021.9549432"},{"key":"1916_CR26","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Malik J, He K: SlowFast Networks for Video Recognition. 2019 IEEECVF Int Conf Comput Vis ICCV 6201\u20136210, 2019","DOI":"10.1109\/ICCV.2019.00630"},{"key":"1916_CR27","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W, Woo W: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proc 29th Int Conf Neural Inf Process Syst - Vol 1 1:802\u2013810, 2015"},{"key":"1916_CR28","doi-asserted-by":"crossref","unstructured":"Chefer H, Gur S, Wolf L: Transformer interpretability beyond attention visualization. 2021 IEEECVF Conf Comput Vis Pattern Recognit CVPR 782\u2013791, 2021","DOI":"10.1109\/CVPR46437.2021.00084"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01916-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-01916-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01916-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:05:16Z","timestamp":1778515516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-01916-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":28,"alternative-id":["1916"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-01916-0","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]},"assertion":[{"value":"24 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study has been granted ethical approval by the Institutional Review Board (IRB) of Shanghai Children\u2019s Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine, under the approval number SCMCIRB-K2024272-1. The study was conducted in strict compliance with the ethical standards outlined in the Declaration of Helsinki. Given the non-invasive approach employed by the research and the anonymization process applied to the data, the IRB has provided an exemption from the requirement for informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study used fully anonymized ultrasound imaging data. According to the institutional review board, informed consent was waived.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}