{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T13:38:10Z","timestamp":1787751490916,"version":"build-2784847793"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Aortic stenosis (AS) is a progressive disease requiring timely monitoring and intervention. While transthoracic echocardiography remains the diagnostic standard, deep learning\u2013based approaches offer the potential for improved disease tracking. This study examined the longitudinal changes in a previously developed deep learning\u2013derived index for AS continuum (DLi\u2010ASc) and assessed its prognostic association with progression to severe AS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">We retrospectively analyzed 2373 patients (7371 transthoracic echocardiographies) from 2 tertiary hospitals. DLi\u2010ASc (scaled 0\u2013100), derived from parasternal long\u2010axis and short\u2010axis views, was tracked longitudinally. The median follow\u2010up duration was 42.8 (interquartile range, 22.2\u201375.7) months.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      DLi\u2010ASc increased in parallel with worsening AS stages (\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      for trend&lt;0.001) and showed strong correlations with aortic valve maximal velocity (Pearson correlation coefficient, 0.69;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001) and mean pressure gradient (Pearson correlation coefficient, 0.66;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001). Higher baseline DLi\u2010ASc was associated with a faster AS progression rate (\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      for trend&lt;0.001). Additionally, the annualized change in DLi\u2010ASc, estimated using linear mixed\u2010effect models, correlated strongly with the annualized progression of aortic valve maximal velocity (Pearson correlation coefficient, 0.71,\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001) and mean pressure gradient (Pearson correlation coefficient, =0.68;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001). In Fine\u2013Gray competing risk models, baseline DLi\u2010ASc was independently associated with progression to severe AS, even after adjustment for aortic valve maximal velocity or mean pressure gradient (hazard ratios per 10\u2010point increase, 2.38 and 2.80, respectively).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">DLi\u2010ASc increased in parallel with AS progression and was independently associated with severe AS progression. These findings support its role as a noninvasive imaging\u2010based digital marker for longitudinal AS monitoring and risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.045179","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T11:45:05Z","timestamp":1768391105000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Longitudinal Validation of a Deep Learning Index for Aortic Stenosis Progression"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-7106","authenticated-orcid":false,"given":"Jiesuck","family":"Park","sequence":"first","affiliation":[{"name":"Seoul National University Bundang Hospital","place":["Seongnam, Republic of Korea"]},{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3419-9955","authenticated-orcid":false,"given":"Jiyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"Ontact Health Inc.","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-9889","authenticated-orcid":false,"given":"Yeonyee E.","family":"Yoon","sequence":"additional","affiliation":[{"name":"Seoul National University Bundang Hospital","place":["Seongnam, Republic of Korea"]},{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]},{"name":"Ontact Health Inc.","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-3449","authenticated-orcid":false,"given":"Jaeik","family":"Jeon","sequence":"additional","affiliation":[{"name":"Ontact Health Inc.","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung\u2010Ah","family":"Lee","sequence":"additional","affiliation":[{"name":"Ontact Health Inc.","place":["Seoul, Republic of Korea"]},{"name":"Yonsei University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6856-2486","authenticated-orcid":false,"given":"Hong\u2010Mi","family":"Choi","sequence":"additional","affiliation":[{"name":"Seoul National University Bundang Hospital","place":["Seongnam, Republic of Korea"]},{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4966-3924","authenticated-orcid":false,"given":"In\u2010Chang","family":"Hwang","sequence":"additional","affiliation":[{"name":"Seoul National University Bundang Hospital","place":["Seongnam, Republic of Korea"]},{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goo\u2010Yeong","family":"Cho","sequence":"additional","affiliation":[{"name":"Seoul National University Bundang Hospital","place":["Seongnam, Republic of Korea"]},{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6139-7545","authenticated-orcid":false,"given":"Hyuk\u2010Jae","family":"Chang","sequence":"additional","affiliation":[{"name":"Ontact Health Inc.","place":["Seoul, Republic of Korea"]},{"name":"Yonsei University College of Medicine","place":["Seoul, Republic of Korea"]},{"name":"Yonsei University Health System","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7035-286X","authenticated-orcid":false,"given":"Jae\u2010Hyeong","family":"Park","sequence":"additional","affiliation":[{"name":"Chungnam National University Hospital","place":["Jung\u2010gu Daejeon, Republic of 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