biostudies-literatureUnknown644(8075)Poterucha TJEarly 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^1,2. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease^3,4, although previous work has been limited by development in narrow populations or targeting only select heart conditions⁵. 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 controlleNature221-230https://www.ebi.ac.uk/biostudies/studies/S-EPMC12328201biostudies-literatureDetecting structural heart disease from electrocardiograms using AI.PMC12328201Bradley CKRuhl JAAdjei-Mosi MPoterucha TJChiu IMMalta PPOuyang DHahn RTBeecy ANRoedan Oliver FALeon MProbst MATastet OCorbin DMaurer MSVolodarskiy AWaksmonski CAElias PHomma SHartzel DLong ACastillo MHaggerty CMJing LKelsey CRosner GFLiu QVaishnava PAli SRRocha DKumaraiah DHartman HSRicart RPTison GHFiner JAvram RSchwartz ADeFilippis EMLebehn MDaniels BvanMaanen DEinstein AJJoshi SDDizon JMYe SHughes JWAgarwal VShames SKampaktsis PNBarrios JPfalseDetecting structural heart disease from electrocardiograms using AI.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^1,2. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease^3,4, although previous work has been limited by development in narrow populations or targeting only select heart conditions⁵. 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 controlle2025-01-01T00:00:00Z2025 Aug2026-04-13T08:13:45.195Z2026-04-07T13:28:42.097ZS-EPMC123282014067079810.1038/s41586-025-09227-0