{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T10:01:09Z","timestamp":1792836069958,"version":"4.5.0"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Johann Wolfgang Goethe-Universit\u00e4t, Frankfurt am Main"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Res Cardiol"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Several software  programs have specifically been developed to analyse cardiac computed tomography prior to transcatheter aortic valve implantation (TAVI). However, they are not able to perform a complete analysis independently. We report the performance of a fully automated, deep learning-based algorithm for pre-procedural CT analysis as compared to the current clinical standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients with symptomatic severe aortic stenosis undergoing TAVI were retrospectively enrolled. The pre-procedural dataset was analysed by both a standard TAVI CT-analysis software and by a fully automated CT analysis platform with a deep learning-based algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Ninety-eight patients were included in the analysis. The mean annulus diameter was 24.4\u2009\u00b1\u20092.4\u00a0mm (conventional\u2009=\u20093mensio, Pie Medical Imaging, 3\u00a0M) vs. 24.0\u2009\u00b1\u20092.4\u00a0mm (artificial intelligence\u2009=\u2009AI), mean absolute error (MAE): 0.64\u00a0mm, mean absolute percentage error (MAPE): 2.6%. The mean annulus perimeter was measured at 77.7\u2009\u00b1\u20097.4\u00a0mm (3\u00a0M) vs. 76.1\u2009\u00b1\u20097.5\u00a0mm (AI), MAE: 2.26\u00a0mm, MAPE: 2.9%. The mean annulus area was calculated at 468.9\u2009\u00b1\u200992.1 mm\n                      <jats:sup>2<\/jats:sup>\n                      (3\u00a0M) vs. 455.6\u2009\u00b1\u200991.0 mm\n                      <jats:sup>2<\/jats:sup>\n                      (AI), MAE: 22.4 mm\n                      <jats:sup>2<\/jats:sup>\n                      , MAPE: 4.8%. The intraclass correlation coefficients (ICCs) of all abovementioned parameter were\u2009&gt;\u20090.95 showing an excellent correlation between the two methods. The distance from the annulus to the left coronary artery depicted to 14.0\u2009\u00b1\u20093.2\u00a0mm (3\u00a0M) vs. 12.6\u2009\u00b1\u20092.8\u00a0mm (AI), MAE: 2.1\u00a0mm, MAPE: 14.3%. The distance to the right coronary artery was 17.1\u2009\u00b1\u20092.7\u00a0mm (3\u00a0M) vs. 16.5\u2009\u00b1\u20093.2\u00a0mm (AI), MAE: 1.7 mm, MAPE: 10.1%. The ICCs of the distances to the coronary ostia showed good correlation between both methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this retrospective analysis, a deep learning-based analysis of pre-procedural CT datasets showed good to excellent correlation with conventional assessment for the preprocedural TAVI CT measurements. AI-based fully automated CT analysis could emerge to a valuable alternative to conventional CT assessment in the pre-procedural-planning for TAVI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00392-025-02790-6","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T08:25:44Z","timestamp":1769588744000},"page":"1199-1206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-assisted computed tomography analysis for pre-procedural planning prior to TAVI"],"prefix":"10.1007","volume":"115","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":"Hanna","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerstin","family":"Piayda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp Christian","family":"Seppelt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Van Linden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Fichtlscherer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Hecker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Leistner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Walther","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"2790_CR1","doi-asserted-by":"crossref","unstructured":"Schultz CJ, Moelker AD, Tzikas A, Rossi A, van Geuns RJ, de Feyter PJ et al (2010) Cardiac CT: necessary for precise sizing for transcatheter aortic implantation. 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