{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:19:02Z","timestamp":1783639142385,"version":"3.55.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"5.Department of Logistics, Molde, University College, Molde, 6410, Norway"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cardiovasc Imaging"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by radiation exposure and availability. Artificial intelligence (AI)-based calcium detection using transthoracic echocardiography has shown promise but depends on acoustic window quality. Transesophageal echocardiography (TEE), particularly 3D TEE, may overcome these limitations by providing improved visualization without radiation. The objective of this study is to evaluate the feasibility of AI-based quantification of aortic valve calcium using 3D TEE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this prospective pilot study, 23 patients (median age, 76\u00a0years; 56.5% male) with moderate or severe aortic stenosis underwent 3D TEE and CT. Multiplanar reconstruction generated 1.5-mm diastolic short-axis slices. A computer vision\u2013based model identified calcium-related speckles. An automated TEE calcium score was derived from the sum of calcium pixels across 11 frames per patient, which was compared with the CT Agatston score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The TEE calcium score showed a significant positive correlation with CT Agatston scores (r\u2009=\u20090.65, P\u2009&lt;\u20090.001). Receiver operating characteristic analysis yielded an area under the curve of 0.87 (95% confidence interval, 0.69\u20131.00) for identifying severe calcification (cutoff, 68,813 pixels; sensitivity, 89.5%; specificity, 75.0%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI-based calcium quantification using 3D TEE is feasible and correlates with CT-derived scores. This radiation-free approach may provide a promising alternative for assessing aortic valve calcification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s44348-026-00080-x","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:01:23Z","timestamp":1783638083000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2013based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study"],"prefix":"10.1186","volume":"34","author":[{"given":"Paula","family":"Fazendas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Bairros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu\u00eds Brito","family":"Elvas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liliana","family":"Brochado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Carlos","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita","family":"Gomes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Rita","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Martins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e2ndida","family":"Louren\u00e7o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1s","family":"Brand\u00e3o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e9lder","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana G.","family":"Almeida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"80_CR1","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/S0140-6736(06)69208-8","volume":"368","author":"VT Nkomo","year":"2006","unstructured":"Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet. 2006;368:1005\u201311.","journal-title":"Lancet"},{"key":"80_CR2","doi-asserted-by":"crossref","unstructured":"Eveborn GW, Schirmer H, Heggelund G, Lunde P, Rasmussen K.\u00a0The evolving epidemiology of valvular aortic stenosis. the Tromso study.\u00a0Heart.\u00a02013;99:396\u2013400.","DOI":"10.1136\/heartjnl-2012-302265"},{"key":"80_CR3","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1016\/j.jacc.2013.05.015","volume":"62","author":"RL Osnabrugge","year":"2013","unstructured":"Osnabrugge RL, Mylotte D, Head SJ, Van Mieghem NM, Nkomo VT, LeReun CM, et al. Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study. J Am Coll Cardiol. 2013;62:1002\u201312.","journal-title":"J Am Coll Cardiol"},{"key":"80_CR4","doi-asserted-by":"publisher","first-page":"4635","DOI":"10.1093\/eurheartj\/ehaf194","volume":"46","author":"F Praz","year":"2025","unstructured":"Praz F, Borger MA, Lanz J, Marin-Cuartas M, Abreu A, Adamo M, et al. 2025 ESC\/EACTS guidelines for the management of valvular heart disease. Eur Heart J. 2025;46:4635\u2013736.","journal-title":"Eur Heart J"},{"key":"80_CR5","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1016\/j.jcmg.2019.01.045","volume":"12","author":"T Pawade","year":"2019","unstructured":"Pawade T, Sheth T, Guzzetti E, Dweck MR, Clavel MA. Why and how to measure aortic valve calcification in patients with aortic stenosis. JACC Cardiovasc Imaging. 2019;12:1835\u201348.","journal-title":"JACC Cardiovasc Imaging"},{"key":"80_CR6","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.117.007146","volume":"11","author":"T Pawade","year":"2018","unstructured":"Pawade T, Clavel MA, Tribouilloy C, Dreyfus J, Mathieu T, Tastet L, et al. Computed tomography aortic valve calcium scoring in patients with aortic stenosis. Circ Cardiovasc Imaging. 2018;11:e007146.","journal-title":"Circ Cardiovasc Imaging"},{"key":"80_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13144064","volume":"13","author":"P Ga\u0107","year":"2024","unstructured":"Ga\u0107 P, Jaworski A, Grajnert F, Kicman K, Trejtowicz-Sutor A, Witkowski K, et al. Aortic valve calcium score: applications in clinical practice and scientific research-a narrative review. J Clin Med. 2024;13:4064.","journal-title":"J Clin Med"},{"key":"80_CR8","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1016\/j.jacc.2013.08.1621","volume":"62","author":"MA Clavel","year":"2013","unstructured":"Clavel MA, Messika-Zeitoun D, Pibarot P, Aggarwal SR, Malouf J, Araoz PA, et al. The complex nature of discordant severe calcified aortic valve disease grading: new insights from combined Doppler echocardiographic and computed tomographic study. J Am Coll Cardiol. 2013;62:2329\u201338.","journal-title":"J Am Coll Cardiol"},{"key":"80_CR9","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1093\/ehjci\/jead153","volume":"24","author":"MR Dweck","year":"2023","unstructured":"Dweck MR, Loganath K, Bing R, Treibel TA, McCann GP, Newby DE, et al. Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document. Eur Heart J Cardiovasc Imaging. 2023;24:1430\u201343.","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"80_CR10","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1093\/ehjci\/jew335","volume":"18","author":"H Baumgartner","year":"2017","unstructured":"Baumgartner H, Hung J, Bermejo J, Chambers JB, Edvardsen T, Goldstein S, et al. Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European Association of Cardiovascular Imaging and the American Society of Echocardiography. Eur Heart J Cardiovasc Imaging. 2017;18:254\u201375.","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"80_CR11","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.amjcard.2017.09.016","volume":"121","author":"T Podlesnikar","year":"2018","unstructured":"Podlesnikar T, Prihadi EA, van Rosendael PJ, Vollema EM, van der Kley F, de Weger A, et al. Influence of the quantity of aortic valve calcium on the agreement between automated 3-dimensional transesophageal echocardiography and multidetector row computed tomography for aortic annulus sizing. Am J Cardiol. 2018;121:86\u201393.","journal-title":"Am J Cardiol"},{"key":"80_CR12","doi-asserted-by":"publisher","first-page":"e119","DOI":"10.1093\/ehjci\/jead090","volume":"24","author":"FF Faletra","year":"2023","unstructured":"Faletra FF, Agricola E, Flachskampf FA, Hahn R, Pepi M, Ajmone Marsan N, et al. Three-dimensional transoesophageal echocardiography: how to use and when to use-a clinical consensus statement from the European Association of Cardiovascular Imaging of the European Society of Cardiology. Eur Heart J Cardiovasc Imaging. 2023;24:e119\u201397.","journal-title":"Eur Heart J Cardiovasc Imaging"},{"key":"80_CR13","volume-title":"Clinical cardiac CT: anatomy and function","year":"2011","unstructured":"Halpern EJ, editor. Clinical cardiac CT: anatomy and function. 2nd ed. Georg Thieme Verlag; 2011.","edition":"2nd ed"},{"key":"80_CR14","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.jcct.2009.07.003","volume":"3","author":"E Lin","year":"2009","unstructured":"Lin E, Alessio A. What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT? J Cardiovasc Comput Tomogr. 2009;3:403\u20138.","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"80_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/app13137479","volume":"13","author":"F Kitsios","year":"2023","unstructured":"Kitsios F, Kamariotou M, Syngelakis AI, Talias MA. Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci. 2023;13:7479.","journal-title":"Appl Sci"},{"key":"80_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44\u201356.","journal-title":"Nat Med"},{"key":"80_CR17","volume":"49","author":"SA Coulter","year":"2022","unstructured":"Coulter SA, Campos K. Artificial intelligence in echocardiography. Tex Heart Inst J. 2022;49:e217671.","journal-title":"Tex Heart Inst J"},{"key":"80_CR18","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1002\/mp.14818","volume":"48","author":"Y Lei","year":"2021","unstructured":"Lei Y, Fu Y, Roper J, Higgins K, Bradley JD, Curran WJ, et al. Echocardiographic image multi-structure segmentation using Cardiac-SegNet. Med Phys. 2021;48:2426\u201337.","journal-title":"Med Phys"},{"key":"80_CR19","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.echo.2022.10.014","volume":"36","author":"W Dai","year":"2023","unstructured":"Dai W, Nazzari H, Namasivayam M, Hung J, Stultz CM. Identifying aortic stenosis with a single parasternal long-axis video using deep learning. J Am Soc Echocardiogr. 2023;36:116\u20138.","journal-title":"J Am Soc Echocardiogr"},{"key":"80_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50735-8","volume":"14","author":"KR Steffner","year":"2024","unstructured":"Steffner KR, Christensen M, Gill G, Bowdish M, Rhee J, Kumaresan A, et al. Deep learning for transesophageal echocardiography view classification. Sci Rep. 2024;14:11.","journal-title":"Sci Rep"},{"key":"80_CR21","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.echo.2023.03.008","volume":"36","author":"H Krishna","year":"2023","unstructured":"Krishna H, Desai K, Slostad B, Bhayani S, Arnold JH, Ouwerkerk W, et al. Fully automated artificial intelligence assessment of aortic stenosis by echocardiography. J Am Soc Echocardiogr. 2023;36:769\u201377.","journal-title":"J Am Soc Echocardiogr"},{"key":"80_CR22","doi-asserted-by":"publisher","first-page":"584","DOI":"10.2174\/1573405615666190114151255","volume":"16","author":"MH Nizar","year":"2020","unstructured":"Nizar MH, Chan CK, Khalil A, Yusof AK, Lai KW. Real-time detection of aortic valve in echocardiography using convolutional neural networks. Curr Med Imaging. 2020;16:584\u201391.","journal-title":"Curr Med Imaging"},{"key":"80_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107882","volume":"243","author":"L Tang","year":"2024","unstructured":"Tang L, Wang X, Yang J, Wang Y, Qu M, Li H. DLFFNet: a new dynamical local feature fusion network for automatic aortic valve calcification recognition using echocardiography. Comput Methods Programs Biomed. 2024;243:107882.","journal-title":"Comput Methods Programs Biomed"},{"key":"80_CR24","doi-asserted-by":"publisher","first-page":"4592","DOI":"10.1093\/eurheartj\/ehad456","volume":"44","author":"G Holste","year":"2023","unstructured":"Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, et al. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J. 2023;44:4592\u2013604.","journal-title":"Eur Heart J"},{"key":"80_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11070598","volume":"11","author":"LB Elvas","year":"2021","unstructured":"Elvas LB, Almeida AG, Rosario L, Dias MS, Ferreira JC. Calcium identification and scoring based on echocardiography. An exploratory study on aortic valve stenosis. J Pers Med. 2021;11:598.","journal-title":"J Pers Med"},{"key":"80_CR26","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1109\/TMI.2023.3305384","volume":"43","author":"N Ahmadi","year":"2024","unstructured":"Ahmadi N, Tsang MY, Gu AN, Tsang TS, Abolmaesumi P. Transformer-based spatio-temporal analysis for classification of aortic stenosis severity from echocardiography cine series. IEEE Trans Med Imaging. 2024;43:366\u201376.","journal-title":"IEEE Trans Med Imaging"},{"key":"80_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0148904","volume":"11","author":"K Gillis","year":"2016","unstructured":"Gillis K, Bala G, Roosens B, Remory I, Hernot S, Droogmans S, et al. Quantification of calcium amount in a new experimental model: a comparison between ultrasound and computed tomography. PLoS ONE. 2016;11:e0148904.","journal-title":"PLoS ONE"},{"key":"80_CR28","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ijcard.2017.07.020","volume":"252","author":"K Gillis","year":"2018","unstructured":"Gillis K, Bala G, Roosens B, Hernot S, Remory I, Scheirlynck E, et al. Clinical validation of an ultrasound quantification score for aortic valve calcifications. Int J Cardiol. 2018;252:68\u201371.","journal-title":"Int J Cardiol"},{"key":"80_CR29","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.21037\/jtd-23-470","volume":"15","author":"Z Liu","year":"2023","unstructured":"Liu Z, Li W, Li H, Zhang F, Ouyang W, Wang S, et al. Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery. J Thorac Dis. 2023;15:2129\u201340.","journal-title":"J Thorac Dis"},{"key":"80_CR30","unstructured":"R Core Team.\u00a0R: a language and environment for statistical computing: the R manuals.\u00a0R Foundation for Statistical Computing. Available from: https:\/\/cran.r-project.org\/manuals.html. Cited DATE."},{"key":"80_CR31","unstructured":"Posit Software. RStudio. Posit Software;. Available from: https:\/\/posit.co\/products\/open-source\/rstudio. Cited DATE."},{"key":"80_CR32","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1161\/01.CIR.83.3.817","volume":"83","author":"WG Daniel","year":"1991","unstructured":"Daniel WG, Erbel R, Kasper W, Visser CA, Engberding R, Sutherland GR, et al. Safety of transesophageal echocardiography. A multicenter survey of 10,419 examinations. Circulation. 1991;83:817\u201321.","journal-title":"Circulation"},{"key":"80_CR33","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1016\/j.crad.2014.05.106","volume":"69","author":"D Jochheim","year":"2014","unstructured":"Jochheim D, Schneider VS, Schwarz F, Kupatt C, Lange P, Reiser M, et al. Contrast-induced acute kidney injury after computed tomography prior to transcatheter aortic valve implantation. Clin Radiol. 2014;69:1034\u20138.","journal-title":"Clin Radiol"},{"key":"80_CR34","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-024-00381-1","volume":"17","author":"LB Elvas","year":"2024","unstructured":"Elvas LB, Gomes S, Ferreira JC, Ros\u00e1rio LB, Brand\u00e3o T. Deep learning for automatic calcium detection in echocardiography. BioData Min. 2024;17:27.","journal-title":"BioData Min"},{"key":"80_CR35","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.jcmg.2022.10.009","volume":"16","author":"N Willner","year":"2023","unstructured":"Willner N, Prosperi-Porta G, Lau L, Nam Fu AY, Boczar K, Poulin A, et al. Aortic stenosis progression: a systematic review and meta-analysis. JACC Cardiovasc Imaging. 2023;16:314\u201328.","journal-title":"JACC Cardiovasc Imaging"},{"key":"80_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9110250","volume":"9","author":"IG Scalia","year":"2023","unstructured":"Scalia IG, Farina JM, Padang R, Jokerst CE, Pereyra M, Mahmoud AK, et al. Aortic valve calcium score by computed tomography as an adjunct to echocardiographic assessment: a review of clinical utility and applications. J Imaging. 2023;9:250.","journal-title":"J Imaging"}],"container-title":["Journal of Cardiovascular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s44348-026-00080-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s44348-026-00080-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s44348-026-00080-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:01:24Z","timestamp":1783638084000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s44348-026-00080-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,10]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["80"],"URL":"https:\/\/doi.org\/10.1186\/s44348-026-00080-x","relation":{},"ISSN":["2586-7210","2586-7296"],"issn-type":[{"value":"2586-7210","type":"print"},{"value":"2586-7296","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,10]]},"assertion":[{"value":"13 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Institutional Ethics Committee of Hospital Garcia de Orta, Unidade Local de Sa\u00fade Almada-Seixal, (No. 04\/2024). All participants provided informed consent. The study protocol complied with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18"}}