{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:26:48Z","timestamp":1783013208646,"version":"3.54.6"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82102041"],"award-info":[{"award-number":["82102041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["20220810145705001"],"award-info":[{"award-number":["20220810145705001"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Fully automated diagnostic echocardiography allows for broader screening, earlier diagnosis for patients with mitral regurgitation (MR). This feasibility study developed a deep learning (DL) framework to automatically detect MR in echocardiography videos and classify regurgitation samples into central or eccentric regurgitation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We designed a Transformer\u2010based deep learning model for fully automatic Doppler video detection of MR. This framework can automatically detect MR and classify central or eccentric regurgitation. The algorithm was trained, validated, and tested using retrospectively selected studies. A prospective dataset of 217 patients was used as an independent test database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>(a) The model demonstrated exceptional diagnostic accuracy for MR, achieving accuracy rates of 0.94(95%CI: 0.90\u20100.97) and 0.92(95%CI: 0.89\u20100.96) in retrospective and prospective test sets, respectively, with area under the curve (AUC) values of 0.98 (95%CI: 0.96\u20101.0) and 0.97 (95%CI: 0.95\u20100.99). Its performance aligned with that of middle seniority physicians. (b) The model achieved an accuracy of 0.93 in identifying central and eccentric MR across both retrospective and prospective test sets, AUC values of 0.96 (95%CI: 0.89\u20100.99) and 0.95 (95%CI: 0.89\u20100.98), respectively. (c) On the tasks of MR, the diagnostic performance of the model in multiple views was better than that of the parasternal long\u2010axis view (PLAX) alone or the apical four\u2010chamber view (A4C) in both retrospective and prospective datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The Transformer\u2010based algorithm proposed in this study can automate and improve the efficiency of clinical workflows, screen multi\u2010view for the presence of MR, and assist in classifying regurgitation characteristics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70584","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T07:02:53Z","timestamp":1776668573000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitral regurgitation detection and central\/eccentric classification using transformer\u2010based deep learning in multi\u2010view echocardiography"],"prefix":"10.1002","volume":"27","author":[{"given":"Xiaofang","family":"Zhong","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiancheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"The National\u2010Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center Shenzhen University Shenzhen China"},{"name":"Medical Ultrasound Image Computing (MUSIC) Laboratory Shenzhen University  Shenzhen China"},{"name":"Marshall Laboratory of Biomedical Engineering Shenzhen University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University)  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[{"name":"The National\u2010Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center Shenzhen University Shenzhen China"},{"name":"Medical Ultrasound Image Computing (MUSIC) Laboratory Shenzhen University  Shenzhen China"},{"name":"Marshall Laboratory of Biomedical Engineering Shenzhen University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; 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