{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lee SY"],"funding":["The Korean Society of Echocardiography"],"pagination":["4"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12914959"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["34(1)"],"pubmed_abstract":["Background
Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence-enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients.Methods
In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area un"],"journal":["Journal of cardiovascular imaging"],"pubmed_title":["Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography."],"pmcid":["PMC12914959"],"funding_grant_id":["KSER-202001"],"pubmed_authors":["Yoo AH","Son JM","Lee HS","Lee SY","Lee MS","Kang S","Kwon JM","Han GI","Kim KH","Jang JH","Jo YY"],"additional_accession":[]},"is_claimable":false,"name":"Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.","description":"Background
Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence-enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients.Methods
In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area un","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T06:58:25.781Z","creation":"2026-07-09T10:41:22.034Z"},"accession":"S-EPMC12914959","cross_references":{"pubmed":["41692786"],"doi":["10.1186/s44348-026-00066-9"]}}