{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:35:11Z","timestamp":1793129711110,"version":"4.5.0"},"reference-count":76,"publisher":"Elsevier BV","issue":"20","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"vor","delay-in-days":391,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacc.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["JACC: Cardiovascular Interventions"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.jcin.2025.08.031","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:21:03Z","timestamp":1761646863000},"page":"2439-2457","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"title":["Artificial Intelligence in Valvular Heart Disease"],"prefix":"10.1016","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-5153","authenticated-orcid":false,"given":"Annette","family":"Maznyczka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rutger-Jan","family":"Nuis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Shiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9629-5864","authenticated-orcid":false,"given":"Julien","family":"Ternacle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Garot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2124-109X","authenticated-orcid":false,"given":"Mark M.P.","family":"van den Dorpel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-2865","authenticated-orcid":false,"given":"Arif A.","family":"Khokhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8222-3977","authenticated-orcid":false,"given":"Raffaele","family":"De Lucia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Orini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shelby","family":"Kutty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4620-6234","authenticated-orcid":false,"given":"Julia","family":"Grapsa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Gr\u00e4ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ambarish","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taylor","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"O\u2019Gallagher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Mortier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3776-0073","authenticated-orcid":false,"given":"Lakshmi Prasad","family":"Dasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus Fuglsang","family":"Kofoed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandy","family":"Engelhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patric","family":"Biaggi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2613-2541","authenticated-orcid":false,"given":"Faraz S.","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dee Dee","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lionel","family":"Leroux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4169-3766","authenticated-orcid":false,"given":"Thomas","family":"Modine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Windecker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1342-9191","authenticated-orcid":false,"given":"Rebecca T.","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas M.","family":"Van Mieghem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ole","family":"De Backer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.jcin.2025.08.031_bib1","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1093\/ehjdh\/ztaf006","article-title":"Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair: the MITRA-AI study","volume":"6","author":"D\u2019Ascenzo","year":"2025","journal-title":"Eur Heart J Digit Health"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib2","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s00259-024-06922-4","article-title":"Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis","volume":"52","author":"Shiri","year":"2025","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2024.101234","article-title":"AI-enhanced prediction of aortic stenosis progression: insights from the PROGRESSA study","volume":"3","author":"Sanabria","year":"2024","journal-title":"JACC Adv"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib4","doi-asserted-by":"crossref","first-page":"e74","DOI":"10.1111\/apa.12062","article-title":"Skills of primary healthcare physicians in paediatric cardiac auscultation","volume":"102","author":"Germanakis","year":"2013","journal-title":"Acta Paediatr"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ihj.2016.07.020","article-title":"Utility of physical examination and comparison to echocardiography for cardiac diagnosis","volume":"69","author":"Patel","year":"2017","journal-title":"Indian Heart J"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib6","first-page":"1","article-title":"Cardiac auscultation versus two-dimensional transthoracic echocardiography for detection of valve disease in Erbil City","volume":"3","author":"Daham","year":"2017","journal-title":"J Heart Cardiol"},{"issue":"9","key":"10.1016\/j.jcin.2025.08.031_bib7","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.amjmed.2022.04.032","article-title":"Artificial intelligence-based stethoscope for the diagnosis of aortic stenosis","volume":"135","author":"Ghanayim","year":"2022","journal-title":"Am J Med"},{"key":"10.1016\/j.jcin.2025.08.031_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijcard.2024.132653","article-title":"External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard","volume":"419","author":"Roquemen-Echeverri","year":"2025","journal-title":"Int J Cardiol"},{"issue":"20","key":"10.1016\/j.jcin.2025.08.031_bib9","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.123.030377","article-title":"Deep learning algorithms to detect murmurs associated with structural heart disease","volume":"12","author":"Prince","year":"2023","journal-title":"J Am Heart Assoc"},{"key":"10.1016\/j.jcin.2025.08.031_bib10","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.amjcard.2023.12.015","article-title":"Meta-analysis of the performance of AI-driven ECG interpretation in the diagnosis of valvular heart diseases","volume":"213","author":"Singh","year":"2024","journal-title":"Am J Cardiol"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib11","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.jacc.2022.05.029","article-title":"Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease","volume":"80","author":"Elias","year":"2022","journal-title":"J Am Coll Cardiol"},{"issue":"30","key":"10.1016\/j.jcin.2025.08.031_bib12","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1093\/eurheartj\/ehab153","article-title":"Electrocardiogram screening for aortic valve stenosis using artificial intelligence","volume":"42","author":"Cohen-Shelly","year":"2021","journal-title":"Eur Heart J"},{"issue":"12","key":"10.1016\/j.jcin.2025.08.031_bib13","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.jacc.2025.01.030","article-title":"Ensemble deep learning algorithm for structural heart disease screening using electrocardiographic images: PRESENT SHD","volume":"85","author":"Dhingra","year":"2025","journal-title":"J Am Coll Cardiol"},{"issue":"12","key":"10.1016\/j.jcin.2025.08.031_bib14","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1016\/j.jacc.2025.02.008","article-title":"Implementing ECG-AI in clinical care: from pixel to practice","volume":"85","author":"Yoon","year":"2025","journal-title":"J Am Coll Cardiol"},{"issue":"3","key":"10.1016\/j.jcin.2025.08.031_bib15","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1093\/ehjdh\/ztae015","article-title":"Artificial intelligence-based classification of echocardiographic views","volume":"5","author":"Naser","year":"2024","journal-title":"Eur Heart J Digit Health"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib16","first-page":"971","article-title":"Temporal views of flattened mitral valve geometries","volume":"26","author":"Eulzer","year":"2020","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"12","key":"10.1016\/j.jcin.2025.08.031_bib17","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1161\/CIRCULATIONAHA.124.068996","article-title":"Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR)","volume":"150","author":"Long","year":"2024","journal-title":"Circulation"},{"issue":"28","key":"10.1016\/j.jcin.2025.08.031_bib18","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.1093\/eurheartj\/ehaf248","article-title":"Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-Regurgitation study","volume":"46","author":"Long","year":"2025","journal-title":"Eur Heart J"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib19","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.recesp.2025.02.005","article-title":"Advanced echocardiography and cluster analysis to identify secondary tricuspid regurgitation phenogroups at different risk","volume":"78","author":"Badano","year":"2025","journal-title":"Rev Esp Cardiol (Engl Ed)"},{"issue":"19","key":"10.1016\/j.jcin.2025.08.031_bib20","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1016\/j.jcin.2021.08.034","article-title":"Subphenotyping of patients with aortic stenosis by unsupervised agglomerative clustering of echocardiographic and hemodynamic data","volume":"14","author":"Lachmann","year":"2021","journal-title":"JACC Cardiovasc Interv"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib21","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol"},{"issue":"4","key":"10.1016\/j.jcin.2025.08.031_bib22","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.jcmg.2019.10.013","article-title":"Enhanced diagnosis of severe aortic stenosis using artificial intelligence: a proof-of-concept study of 530,871 echocardiograms","volume":"13","author":"Playford","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"issue":"9","key":"10.1016\/j.jcin.2025.08.031_bib23","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1016\/j.jcmg.2021.03.020","article-title":"A machine-learning framework to identify distinct phenotypes of aortic stenosis severity","volume":"14","author":"Sengupta","year":"2021","journal-title":"JACC Cardiovasc Imaging"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib24","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1001\/jamacardio.2025.0498","article-title":"Automated deep learning phenotyping of tricuspid regurgitation in echocardiography","volume":"10","author":"Vrudhula","year":"2025","journal-title":"JAMA Cardiol"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib25","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1001\/jamacardio.2024.0595","article-title":"A multimodal video-based AI biomarker for aortic stenosis development and progression","volume":"9","author":"Oikonomou","year":"2024","journal-title":"JAMA Cardiol"},{"issue":"5","key":"10.1016\/j.jcin.2025.08.031_bib26","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCIMAGING.119.009707","article-title":"Unsupervised cluster analysis of patients with aortic stenosis reveals distinct population with different phenotypes and outcomes","volume":"13","author":"Kwak","year":"2020","journal-title":"Circ Cardiovasc Imaging"},{"issue":"12","key":"10.1016\/j.jcin.2025.08.031_bib27","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1016\/j.jcmg.2021.05.020","article-title":"Principal morphomic and functional components of secondary mitral regurgitation","volume":"14","author":"Bartko","year":"2021","journal-title":"JACC Cardiovasc Imaging"},{"issue":"10 suppl","key":"10.1016\/j.jcin.2025.08.031_bib28","article-title":"Deep learning analysis of aortic valve and coronary artery calcium scores from non-gated computed tomography scans: comparison with the ECG-gated reference standard","volume":"40","author":"Bendayan","year":"2024","journal-title":"Can J Cardiol"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib29","doi-asserted-by":"crossref","first-page":"8472","DOI":"10.1038\/s41598-025-93744-5","article-title":"Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography","volume":"15","author":"Park","year":"2025","journal-title":"Sci Rep"},{"issue":"10436","key":"10.1016\/j.jcin.2025.08.031_bib30","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1016\/S0140-6736(23)02754-X","article-title":"The future of valvular heart disease assessment and therapy","volume":"403","author":"Sengupta","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.jcin.2025.08.031_bib31","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2024.1343210","article-title":"Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis","volume":"11","author":"Sazzad","year":"2024","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib32","doi-asserted-by":"crossref","DOI":"10.1136\/openhrt-2022-001990","article-title":"Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score","volume":"9","author":"Namasivayam","year":"2022","journal-title":"Open Heart"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib33","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.jacc.2021.05.047","article-title":"Markers of myocardial damage predict mortality in patients with aortic stenosis","volume":"78","author":"Kwak","year":"2021","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jcin.2025.08.031_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcct.2025.01.008","article-title":"Novel CT derived markers for enhanced long-term risk stratification in the planning of TAVR for aortic stenosis","author":"Lorenzatti","year":"2025","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"9","key":"10.1016\/j.jcin.2025.08.031_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2024.101135","article-title":"Machine learning prediction for prognosis of patients with aortic stenosis","volume":"3","author":"Shimoni","year":"2024","journal-title":"JACC Adv"},{"issue":"11","key":"10.1016\/j.jcin.2025.08.031_bib36","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1093\/eurheartj\/ehad871","article-title":"Artificial intelligence-derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge-to-edge repair: the EuroSMR risk score","volume":"45","author":"Hausleiter","year":"2024","journal-title":"Eur Heart J"},{"key":"10.1016\/j.jcin.2025.08.031_bib37","series-title":"MitrAI score: machine learning models to predict mortality at 12, 24, 36 and 48 months in patients with primary and secondary mitral regurgitation treated by M-TEER","author":"Biasco","year":"2024"},{"issue":"18","key":"10.1016\/j.jcin.2025.08.031_bib38","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1016\/j.jcin.2021.06.039","article-title":"Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair","volume":"14","author":"Zweck","year":"2021","journal-title":"JACC Cardiovasc Interv"},{"key":"10.1016\/j.jcin.2025.08.031_bib39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.carrev.2024.03.008","article-title":"A fully automated artificial intelligence-driven software for planning of transcatheter aortic valve replacement","volume":"65","author":"Toggweiler","year":"2024","journal-title":"Cardiovasc Revasc Med"},{"key":"10.1016\/j.jcin.2025.08.031_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2023.104794","article-title":"Development and validation of a deep learning-based fully automated algorithm for pre-TAVR CT assessment of the aortic valvular complex and detection of anatomical risk factors: a retrospective, multicentre study","volume":"96","author":"Wang","year":"2023","journal-title":"EBioMedicine"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib41","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCIMAGING.119.009178","article-title":"Patient-specific computer simulation of transcatheter aortic valve replacement in bicuspid aortic valve morphology","volume":"12","author":"Dowling","year":"2019","journal-title":"Circ Cardiovasc Imaging"},{"issue":"5","key":"10.1016\/j.jcin.2025.08.031_bib42","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.jcin.2016.01.003","article-title":"Patient-specific computer modeling to predict aortic regurgitation after transcatheter aortic valve replacement","volume":"9","author":"de Jaegere","year":"2016","journal-title":"JACC Cardiovasc Interv"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib43","first-page":"1140","article-title":"Clinical value of CT-derived simulations of transcatheter-aortic-valve-implantation in challenging anatomies the PRECISE-TAVI trial","volume":"102","author":"Hokken","year":"2023","journal-title":"Catheter Cardiovasc Interv"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib44","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s10439-023-03360-5","article-title":"Reduced order modeling for real-time stent deformation simulations of transcatheter aortic valve prostheses","volume":"52","author":"Shah","year":"2024","journal-title":"Ann Biomed Eng"},{"key":"10.1016\/j.jcin.2025.08.031_bib45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.xjtc.2023.11.011","article-title":"Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement","volume":"23","author":"Dasi","year":"2024","journal-title":"JTCVS Tech"},{"key":"10.1016\/j.jcin.2025.08.031_bib46","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ahj.2023.12.017","article-title":"Preoperative computed tomography-imaging with patient-specific computer simulation in transcatheter aortic valve implantation: design and rationale of the GUIDE-TAVI trial","volume":"269","author":"Hegeman","year":"2024","journal-title":"Am Heart J"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib47","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-76128-z","article-title":"Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data","volume":"14","author":"El Ouahidi","year":"2024","journal-title":"Sci Rep"},{"issue":"8","key":"10.1016\/j.jcin.2025.08.031_bib48","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.jcin.2024.11.023","article-title":"Lifetime management in transcatheter aortic valve replacement through patient-specific simulations","volume":"18","author":"van den Dorpel","year":"2025","journal-title":"JACC Cardiovasc Interv"},{"issue":"9","key":"10.1016\/j.jcin.2025.08.031_bib49","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1109\/TMI.2010.2048756","article-title":"Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE","volume":"29","author":"Ionasec","year":"2010","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib50","first-page":"379","article-title":"Validating a prediction modeling tool for left ventricular outflow tract (LVOT) obstruction after transcatheter mitral valve replacement (TMVR)","volume":"92","author":"Wang","year":"2018","journal-title":"Catheter Cardiovasc Interv"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib51","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.jcmg.2024.05.011","article-title":"3-Dimensional echocardiographic prediction of left ventricular outflow tract area prior to transcatheter mitral valve replacement","volume":"17","author":"Bartkowiak","year":"2024","journal-title":"JACC Cardiovasc Imaging"},{"key":"10.1016\/j.jcin.2025.08.031_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104767","article-title":"Comparative computational analysis of PASCAL and MitraClip implantation in a patient-specific functional mitral regurgitation model","volume":"136","author":"Errthum","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jcin.2025.08.031_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2020.109730","article-title":"Finite element analysis of MitraClip procedure on a patient-specific model with functional mitral regurgitation","volume":"104","author":"Kong","year":"2020","journal-title":"J Biomech"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib54","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/j.jcin.2023.01.008","article-title":"Impact of computational modeling on transcatheter left atrial appendage closure efficiency and outcomes","volume":"16","author":"De Backer","year":"2023","journal-title":"JACC Cardiovasc Interv"},{"issue":"6-7","key":"10.1016\/j.jcin.2025.08.031_bib55","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.acvd.2018.02.001","article-title":"Usefulness of echocardiographic-fluoroscopic fusion imaging in adult structural heart disease","volume":"111","author":"Ternacle","year":"2018","journal-title":"Arch Cardiovasc Dis"},{"issue":"11","key":"10.1016\/j.jcin.2025.08.031_bib56","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1016\/j.jcin.2020.02.011","article-title":"Transcatheter mitral valve replacement guided by echocardiographic-CT scan fusion: early human clinical experience","volume":"13","author":"Coisne","year":"2020","journal-title":"JACC Cardiovasc Interv"},{"issue":"6-7","key":"10.1016\/j.jcin.2025.08.031_bib57","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.acvd.2018.03.001","article-title":"Impact of fused computed tomography and fluoroscopy in the catheterization laboratory","volume":"111","author":"Fresse-Warin","year":"2018","journal-title":"Arch Cardiovasc Dis"},{"issue":"3","key":"10.1016\/j.jcin.2025.08.031_bib58","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1001\/jamacardio.2022.5016","article-title":"Effectiveness of simulation-based training on transesophageal echocardiography learning: the SIMULATOR randomized clinical trial","volume":"8","author":"Pezel","year":"2023","journal-title":"JAMA Cardiol"},{"issue":"3","key":"10.1016\/j.jcin.2025.08.031_bib59","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2024.100839","article-title":"First experience with augmented reality guidance for cerebral embolic protection during TAVR","volume":"3","author":"Sadri","year":"2024","journal-title":"JACC Adv"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib60","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/JBHI.2021.3099858","article-title":"Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation","volume":"26","author":"Sharan","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib61","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1016\/j.jcmg.2020.05.029","article-title":"Identifying cardiac amyloid in aortic stenosis: ECV quantification by CT in TAVR patients","volume":"13","author":"Scully","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"issue":"7","key":"10.1016\/j.jcin.2025.08.031_bib62","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1001\/jamacardio.2024.0911","article-title":"Osteosarcopenia and mortality in older adults undergoing transcatheter aortic valve replacement","volume":"9","author":"Solla-Suarez","year":"2024","journal-title":"JAMA Cardiol"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib63","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCGEN.120.003014","article-title":"Cardiac imaging of aortic valve area from 34 287 UK Biobank participants reveals novel genetic associations and shared genetic comorbidity with multiple disease phenotypes","volume":"13","author":"Cordova-Palomera","year":"2020","journal-title":"Circ Genom Precis Med"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib64","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1093\/ehjdh\/ztae089","article-title":"Early discharge programme after transcatheter aortic valve implantation based on close follow-up supported by telemonitoring using artificial intelligence: the TeleTAVI study","volume":"6","author":"Herrero-Brocal","year":"2025","journal-title":"Eur Heart J Digit Health"},{"issue":"48","key":"10.1016\/j.jcin.2025.08.031_bib65","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1093\/eurheartj\/ehaa159","article-title":"The 'Digital Twin' to enable the vision of precision cardiology","volume":"41","author":"Corral-Acero","year":"2020","journal-title":"Eur Heart J"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib66","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s41746-022-00640-7","article-title":"The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field","volume":"5","author":"Coorey","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"29","key":"10.1016\/j.jcin.2025.08.031_bib67","doi-asserted-by":"crossref","DOI":"10.1126\/scirobotics.aaw1977","article-title":"Autonomous robotic intracardiac catheter navigation using haptic vision","volume":"4","author":"Fagogenis","year":"2019","journal-title":"Sci Robot"},{"issue":"6","key":"10.1016\/j.jcin.2025.08.031_bib68","doi-asserted-by":"crossref","DOI":"10.1016\/j.shj.2024.100352","article-title":"Concept, design, and preclinical testing of a remote-control robotic system for transesophageal echocardiography","volume":"8","author":"Schewel","year":"2024","journal-title":"Struct Heart"},{"issue":"3","key":"10.1016\/j.jcin.2025.08.031_bib69","article-title":"A systematic review of cardiac in-silico clinical trials","volume":"5","author":"Rodero","year":"2023","journal-title":"Prog Biomed Eng (Bristol)"},{"issue":"2","key":"10.1016\/j.jcin.2025.08.031_bib70","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1093\/ehjqcco\/qcad028","article-title":"Comparative analysis of machine learning vs. traditional modeling approaches for predicting in-hospital mortality after cardiac surgery: temporal and spatial external validation based on a nationwide cardiac surgery registry","volume":"10","author":"Zeng","year":"2024","journal-title":"Eur Heart J Qual Care Clin Outcomes"},{"issue":"10","key":"10.1016\/j.jcin.2025.08.031_bib71","doi-asserted-by":"crossref","first-page":"e749","DOI":"10.1016\/S2589-7500(24)00155-9","article-title":"Mitigating the risk of artificial intelligence bias in cardiovascular care","volume":"6","author":"Mihan","year":"2024","journal-title":"Lancet Digit Health"},{"issue":"3Part B","key":"10.1016\/j.jcin.2025.08.031_bib72","article-title":"Validation study of two artificial intelligence-based preplanning methods for transcatheter aortic valve replacement procedures","volume":"3","author":"Corbin","year":"2024","journal-title":"J Soc Cardiovasc Angiogr Interv"},{"key":"10.1016\/j.jcin.2025.08.031_bib73","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtcvs.2025.01.008","article-title":"Comparing innovative artificial intelligence algorithms to assess echocardiographic videos for clinical modeling","author":"Laldin","year":"2025","journal-title":"J Thorac Cardiovasc Surg"},{"key":"10.1016\/j.jcin.2025.08.031_bib74","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2024.1399138","article-title":"Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies","volume":"11","author":"Yordanov","year":"2024","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib75","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/s41746-025-01434-3","article-title":"Real world federated learning with a knowledge distilled transformer for cardiac CT imaging","volume":"8","author":"Tolle","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"1","key":"10.1016\/j.jcin.2025.08.031_bib76","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-81732-0","article-title":"Issues in federated learning: some experiments and preliminary results","volume":"14","author":"Bhanbhro","year":"2024","journal-title":"Sci Rep"}],"container-title":["JACC: Cardiovascular Interventions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1936879825022666?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1936879825022666?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T03:30:02Z","timestamp":1793071802000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1936879825022666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":76,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["S1936879825022666"],"URL":"https:\/\/doi.org\/10.1016\/j.jcin.2025.08.031","relation":{},"ISSN":["1936-8798"],"issn-type":[{"value":"1936-8798","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in Valvular Heart Disease","name":"articletitle","label":"Article Title"},{"value":"JACC: Cardiovascular Interventions","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jcin.2025.08.031","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 by the American College of Cardiology Foundation. Published by Elsevier.","name":"copyright","label":"Copyright"}]}}