{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sengupta PP"],"funding":["NSF","British Heart Foundation","Fonds de Recherche du Québec - Santé","National Institutes of Health","Wellcome Trust","National Institute of General Medical Sciences","Canadian Institutes of Health Research","NIGMS NIH HHS","National Science Foundation"],"pagination":["1707-1720"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8434951"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(9)"],"pubmed_abstract":["<h4>Objectives</h4>The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity.<h4>Background</h4>In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty.<h4>Methods</h4>Using echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT co"],"journal":["JACC. Cardiovascular imaging"],"pubmed_title":["A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity."],"pmcid":["PMC8434951"],"funding_grant_id":["15/JTA","CH/09/002","5U54GM104942-04","RG/16/10/32375","CH/09/002/26360","WT103782AIA","RE/18/5/34216","FS/14/78/31020","1920920","FS/16/19/31982","U54 GM104942"],"pubmed_authors":["Shen M","Tribouilloy C","Guzzetti E","Bing R","Sengupta PP","Gay LG","Cote N","Mathieu T","Lacomis JM","Pibarot P","Macron L","Gun M","Sechrist JW","Dweck MR","Kulkarni H","Dreyfus J","Beaudoin J","Pawade TA","Calabria HC","Kagiyama N","Nguyen V","Chin CWL","Arsenault M","Tastet L","Evangelista A","Bernier M","Cavalcante JL","Renard C","Messika-Zeitoun D","Clavel MA","Hamirani Y","Yanamala N","Artificial Intelligence for Aortic Stenosis at Risk International Consortium","Newby DE","Ntalas I","Larose E","Everett R","Jenkins WSA","Rajani R","Prendergast B","Shrestha S"],"additional_accession":[]},"is_claimable":false,"name":"A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity.","description":"<h4>Objectives</h4>The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity.<h4>Background</h4>In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty.<h4>Methods</h4>Using echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT co","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2025-04-04T09:37:18.703Z","creation":"2025-04-04T09:37:18.703Z"},"accession":"S-EPMC8434951","cross_references":{"pubmed":["34023273"],"doi":["10.1016/j.jcmg.2021.03.020"]}}