{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:17:12Z","timestamp":1782962232316,"version":"3.54.5"},"reference-count":30,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mitral valve (MV) diseases constitute one of the etiologies of cardiovascular mortality and morbidity. MV pathologies need evaluating and classifying via echocardiographic videos. Transformers have significantly advanced video analytics. MV motion is divided by Carpentier functional classification into four types: normal, increased, restricted, and restricted only during systole. This paper introduces CarpNet, a deep transformer network that incorporates video transformers capable of direct MV pathology Carpentier's classification from the parasternal long\u2010axis (PLA) echocardiographic videos. The network, instead of processing frames independently, analyzes stacks of temporally consecutive frames using multi\u2010head attention modules to incorporate MV temporal dynamics into the learned model. To that end, different convolutional neural networks (CNNs) are evaluated as the backbone, and the best model is selected using the information of the PLA view. The use of information obtained by our proposed deep transformer network from consecutive echocardiographic frames yielded better results concerning the Carpentier functional classification than information obtained by CNN\u2010based (single\u2010frame) models. Using the Inception_Resnet_V2 architecture as the backbone, CarpNet achieved 71% accuracy in the test dataset. Deep learning and transformers in echocardiographic videos can render quick, precise, and stable evaluations of various MV pathologies.<\/jats:p>","DOI":"10.1002\/ima.22885","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T03:32:09Z","timestamp":1680665529000},"page":"1505-1514","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["<scp>CarpNet<\/scp>\n                    : Transformer for mitral valve disease classification in echocardiographic videos"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0172-7784","authenticated-orcid":false,"given":"Majid","family":"Vafaeezadeh","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, School of Electrical Engineering Iran University of Science and Technology  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1698-5284","authenticated-orcid":false,"given":"Hamid","family":"Behnam","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, School of Electrical Engineering Iran University of Science and Technology  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Hosseinsabet","sequence":"additional","affiliation":[{"name":"Cardiology Department, Tehran Heart Center Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-7714-6110","authenticated-orcid":false,"given":"Parisa","family":"Gifani","sequence":"additional","affiliation":[{"name":"Medical Sciences and Technologies Department Science and Research Branch, Islamic Azad University  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.11.035"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23104-0"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0735-1097(19)32613-0"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.04.031"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.15420\/ecr.2014.9.1.49"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.04.002"},{"key":"e_1_2_9_8_1","unstructured":"ShamshadF KhanS ZamirSW et al.Transformers in medical imaging: a survey. arXiv preprint arXiv:2201098732022."},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-021-02542-7"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00376-2"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jormas.2019.06.002"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0216-8"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102248"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0065-x"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2018.07.024"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.118.034338"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2888807"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"FioritoAM \u00d8stvikA SmistadE LeclercS BernardO LovstakkenL.Detection of cardiac events in echocardiography using 3D convolutional recurrent neural networks. 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