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To aid providers\u2019 decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2\u00a0h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF). In our primary analysis, we classified patients into groups of TnI\u2009&lt;\u20090.02 or\u2009\u2265\u20090.02\u00a0\u00b5g\/L using 12-lead ECGs. This was repeated with an alternative threshold of 1.0\u00a0\u00b5g\/L and with single-lead ECG inputs. We also performed multiclass prediction for a set of serum troponin ranges. Finally, we tested the CNN in a cohort of patients selected for coronary angiography, including 3038 ECGs from 672 patients. Cohort patients were 49.0% female, 42.8% white, and 59.3% (19,283) never had a positive TnI value (\u2265\u20090.02\u00a0\u00b5g\/L). CNNs accurately predicted elevated TnI, both at a threshold of 0.02\u00a0\u00b5g\/L (AUC\u2009=\u20090.783, 95% CI 0.780\u20130.786) and at a threshold of 1.0\u00a0\u00b5g\/L (AUC\u2009=\u20090.802, 0.795\u20130.809). Models using single-lead ECG data achieved significantly lower accuracy, with AUCs ranging from 0.740 to 0.773 with variation by lead. Accuracy of the multi-class model was lower for intermediate TnI value-ranges. Our models performed similarly on the cohort of patients who underwent coronary angiography. Biomarker-defined myocardial injury can be predicted by CNNs from 12-lead and single-lead ECGs.<\/jats:p>","DOI":"10.1038\/s41598-023-29989-9","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T08:02:33Z","timestamp":1677484953000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury"],"prefix":"10.1038","volume":"13","author":[{"given":"Gunvant R.","family":"Chaudhari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob J.","family":"Mayfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua P.","family":"Barrios","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Abreau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Avram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey E.","family":"Olgin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey H.","family":"Tison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"29989_CR1","unstructured":"Rui, P. & Kang, K. 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Dr. Olgin has received research funding from the National Institute of Health (U2CEB021881) and from Samsung and iBeat. There are no disclosures for the remaining authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3364"}}