{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T11:09:11Z","timestamp":1784113751815,"version":"3.55.0"},"reference-count":25,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,2,3]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Transcatheter edge\u2010to\u2010edge mitral valve repair is a key therapeutic option for patients with severe symptomatic mitral regurgitation at high surgical risk. This prospective study aimed to develop a novel end\u2010to\u2010end deep learning model for preoperative artificial intelligence assessment in transcatheter edge\u2010to\u2010edge mitral valve repair (TEERAI\u2010pre) candidates using multiview, multimodal echocardiography.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">TEERAI\u2010pre, a video vision transformer\u2013based classification model, predicts morphological suitability for transcatheter edge\u2010to\u2010edge mitral valve repair from multiview, multimodal echocardiography. A transformer\u2010based feature\u2010level fusion module was designed in TEERAI\u2010pre to integrate multiview, multimodal features for final prediction. An internal data set of 633 patients (7997 transthoracic echocardiographic videos; 766 pulsed\u2010wave Doppler images) was split for 5\u2010fold cross\u2010validation. An external data set of 150 patients (1735 transthoracic echocardiographic videos; 169 pulsed\u2010wave Doppler images) across 2 hospitals evaluated generalizability. Reference standards were provided by 2 experienced valvular cardiologists per international guidelines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">On the internal data set, TEERAI\u2010pre achieved 75.0% accuracy (95% CI, 71.7%\u201378.4%) for classifying red (unsuitable), yellow (challenging), and green (ideal) zones, with 77.1% precision, 75.5% recall, and 76.2% F1 score. External validation yielded 73.3% accuracy, 74.0% precision, and 74.0% recall. Multiview multimodal integration improved performance. Binary classification (red versus green) showed TEERAI\u2010pre matched senior experts and outperformed intermediate\/junior echocardiologists. Feature\u2010level fusion outperformed output\u2010level fusion and single\u2010view model. Backbone selection and calibration analysis confirmed robust performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">TEERAI\u2010pre demonstrates strong performance in transcatheter edge\u2010to\u2010edge mitral valve repair preoperative assessment using transthoracic echocardiographic videos and images, supporting more accurate patient selection and enhancing clinical workflow efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Registration<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      URL:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/clinicaltrials.gov\">clinicaltrials.gov<\/jats:ext-link>\n                      ; Unique Identifier: NCT05508438.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.044333","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:11:43Z","timestamp":1769767903000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["TEERAI\u2010Pre: A Multiview Artificial Intelligence Model for Preoperative Assessment of Transcatheter Edge\u2010to\u2010Edge Mitral Valve Repair Using Multiview, Multimodal Echocardiography"],"prefix":"10.1161","volume":"15","author":[{"given":"Hui","family":"Li","sequence":"first","affiliation":[{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yida","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen University","place":["Shenzhen, P.R. 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