{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:11:23Z","timestamp":1780420283366,"version":"3.54.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":138,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009123","name":"Norwegian University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009123","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006397","name":"NTNU","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006397","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaison Committee"},{"DOI":"10.13039\/501100004590","name":"Central Norway Regional Health Authority","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004590","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011076","name":"Simon Fougner Hartmann Family Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011076","id-type":"DOI","asserted-by":"publisher"}]},{"name":"St. Olavs University hospital"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Mitral annular plane systolic excursion (MAPSE) is an accessible echocardiographic measure of left ventricular (LV) function. However, manual measurement methods are operator-dependent and time-consuming. We developed a multistep deep learning (DL) method for off-line and real-time fully automated MAPSE estimation, and aimed to assess agreement, reproducibility, time efficiency, and feasibility compared with standard manual measurements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>The DL-based method was evaluated in two retrospective cohorts (n = 1775) and one prospective cohort (n = 51). Agreement between DL-MAPSE on B-mode images and experts\u2019 manual M-mode measurements was evaluated in all datasets. Evaluation of test\u2013retest reproducibility, time efficiency using real-time analysis during acquisition, and agreement with cardiac magnetic resonance (CMR)-imaging were performed in subsets of the datasets. DL-MAPSE demonstrated good agreement with manual measurements, with bias 2.9\u2005mm (95% CI 2.8\u20133.0\u2005mm) and Pearson coefficient 0.81 (95% CI 0.79\u20130.84) in the primary dataset, and a lower bias of 1.0\u2005mm against CMR-MAPSE compared with \u22122.1\u2005mm using manual M-mode. Both DL and manual measurements showed good test\u2013retest reproducibility (ICC 0.82 and 0.76, respectively). Real-time DL measurements reduced measurement and acquisition time by 51% (mean 1\u2005min 50\u2005s) per examination. The DL method demonstrated excellent feasibility (96%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This novel DL method for fully automated MAPSE demonstrated excellent feasibility, robust reproducibility, and good agreement with both manual M-mode and CMR-derived measurements. Automated DL-MAPSE could substantially reduce analysis time and enhance reproducibility, increasing its clinical value as a marker of LV systolic function.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjimp\/qyag087","type":"journal-article","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T11:32:03Z","timestamp":1779017523000},"source":"Crossref","is-referenced-by-count":0,"title":["Fully automated deep learning MAPSE: retrospective analysis and real-time clinical application"],"prefix":"10.1093","volume":"4","author":[{"given":"Maria Muan","family":"Haga","sequence":"first","affiliation":[{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science and Technology , Box 8905 Trondheim 7491 ,","place":["Norway"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nora Lindeman","family":"Katla","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science 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Technology , Box 8905 Trondheim 7491 ,","place":["Norway"]},{"name":"Department of Cardiology and Thoracic Surgery, St.Olavs University Hospital , Trondheim ,","place":["Norway"]},{"name":"Medical Image Analysis, Health Research, SINTEF Digital, Trondheim ,","place":["Norway"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2474-9588","authenticated-orcid":false,"given":"Lasse","family":"L\u00f8vstakken","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science and Technology , Box 8905 Trondheim 7491 ,","place":["Norway"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4000-6298","authenticated-orcid":false,"given":"H\u00e5vard","family":"Dalen","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Norwegian University of Science and Technology , Box 8905 Trondheim 7491 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