{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/age-prediction-from-12-lead","title":"Age Prediction From 12-lead Electrocardiograms Using Deep Learning: A Comparison of Four Models on a Contemporary, Freely Available Dataset","arxiv_id":null,"date":"2024-02-03","proceeding":"medRiv 2024 2","authors":["Andrew Barros","Ian German-Mesner","N. Rich Nguyen","J. Randall Moorman"],"abstract":"The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. ECG predicted age has been identified as a predictor of long-term mortality. Here we compare four models for age and sex prediction on a contemporary, freely available dataset.","url_abs":"https://www.medrxiv.org/content/10.1101/2024.02.02.24302201v1","url_pdf":"https://www.medrxiv.org/content/10.1101/2024.02.02.24302201v1.full.pdf+html","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"age-prediction-from-12-lead","repo_url":"https://github.com/UVA-CAMA/EcgAgePrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}