{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T07:14:08Z","timestamp":1788246848416,"version":"build-2803163510"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum Frankfurt"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Res Cardiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate pre-procedural computed tomography (CT) analysis is essential for optimal valve sizing and clinical outcomes in transcatheter aortic valve implantation (TAVI). Recently, fully automated, artificial intelligence (AI)-based CT analysis platforms have been developed to simplify and standardize this process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The aim of the study was to investigate the clinical impact of this new analysis method on the selection of valve prosthesis size.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Overall, 247 patients with symptomatic severe aortic stenosis were enrolled. Patients underwent TAVI procedures at two different heart centres. The pre-procedural datasets were analysed by a standard TAVI CT-analysis software (3M, Pie Medical Imaging BV, The Netherlands) and a fully-automated CT-analysis-platform employing a deep-learning based algorithm. Key annular measurements and simulated prosthesis size selection were compared between both methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean aortic annulus diameter was 24.5\u2009\u00b1\u20092.3\u202fmm (3mensio) and 24.4\u2009\u00b1\u20092.4\u202fmm (AI), respectively, with a mean absolute error (MAE) of 0.6\u202fmm and mean absolute percentage error (MAPE) of 2.6%. Annulus perimeter (76.9\u2009\u00b1\u20097.0\u202fmm vs. 74.6\u2009\u00b1\u20097.3\u202fmm; MAE: 2.0\u202fmm; MAPE: 2.6%) and annulus area (458.4\u2009\u00b1\u200987.2\u202fmm\n                      <jats:sup>2<\/jats:sup>\n                      vs. 440.9\u2009\u00b1\u200985.6\u202fmm\n                      <jats:sup>2<\/jats:sup>\n                      ; MAE: 21.2\u202fmm\n                      <jats:sup>2<\/jats:sup>\n                      ; MAPE: 4.6%) showed excellent correlation (intraclass correlation coefficients\u2009&gt;\u20090.95). Prosthesis size selection simulated on the basis of AI-derived measurements would have differed from the implanted size in 21% of patients, compared with 14% when using the semi-automated method.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this retrospective study, fully automated AI-based CT analysis demonstrated excellent agreement with conventional semi-automated measurements of the aortic annulus. Nevertheless, similar to established planning workflows, expert interpretation remains crucial to integrate the broader anatomical and clinical context required for optimal prosthesis size selection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00392-026-03017-y","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:30:34Z","timestamp":1788244234000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of fully-automated AI based CT-analysis on pre-procedural TAVI planning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5861-891X","authenticated-orcid":false,"given":"Mani","family":"Arsalan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tami","family":"Duske","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander R.","family":"Tamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp Christian","family":"Seppelt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Geyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerstin","family":"Piayda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralph Stephan","family":"von Bardeleben","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Leistner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michaela","family":"Hell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Walther","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Kreidel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"issue":"3","key":"3017_CR1","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.athoracsur.2010.09.060","volume":"91","author":"V Delgado","year":"2011","unstructured":"Delgado V, Ng AC, Schuijf JD (2011) Automated assessment of the aortic root dimensions with multidetector row computed tomography. 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