{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["644(8075)"],"submitter":["Poterucha TJ"],"pubmed_abstract":["Early detection of structural heart disease is critical to improving outcomes, but widespread screening remains limited by the cost and accessibility of imaging tools such as echocardiography<sup>1,2</sup>. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease<sup>3,4</sup>, although previous work has been limited by development in narrow populations or targeting only select heart conditions<sup>5</sup>. Here we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system to detect many forms of structural heart disease. The model demonstrated high diagnostic accuracy in internal and external validation, outperforming cardiologists in a controlle"],"journal":["Nature"],"pagination":["221-230"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12328201"],"repository":["biostudies-literature"],"pubmed_title":["Detecting structural heart disease from electrocardiograms using AI."],"pmcid":["PMC12328201"],"pubmed_authors":["Bradley CK","Ruhl JA","Adjei-Mosi M","Poterucha TJ","Chiu IM","Malta PP","Ouyang D","Hahn RT","Beecy AN","Roedan Oliver FA","Leon M","Probst MA","Tastet O","Corbin D","Maurer MS","Volodarskiy A","Waksmonski CA","Elias P","Homma S","Hartzel D","Long A","Castillo M","Haggerty CM","Jing L","Kelsey C","Rosner GF","Liu Q","Vaishnava P","Ali SR","Rocha D","Kumaraiah D","Hartman HS","Ricart RP","Tison GH","Finer J","Avram R","Schwartz A","DeFilippis EM","Lebehn M","Daniels B","vanMaanen D","Einstein AJ","Joshi SD","Dizon JM","Ye S","Hughes JW","Agarwal V","Shames S","Kampaktsis PN","Barrios JP"],"additional_accession":[]},"is_claimable":false,"name":"Detecting structural heart disease from electrocardiograms using AI.","description":"Early detection of structural heart disease is critical to improving outcomes, but widespread screening remains limited by the cost and accessibility of imaging tools such as echocardiography<sup>1,2</sup>. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease<sup>3,4</sup>, although previous work has been limited by development in narrow populations or targeting only select heart conditions<sup>5</sup>. Here we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system to detect many forms of structural heart disease. The model demonstrated high diagnostic accuracy in internal and external validation, outperforming cardiologists in a controlle","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-04-13T08:13:45.195Z","creation":"2026-04-07T13:28:42.097Z"},"accession":"S-EPMC12328201","cross_references":{"pubmed":["40670798"],"doi":["10.1038/s41586-025-09227-0"]}}