{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:22:35Z","timestamp":1772187755478,"version":"3.50.1"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged \u226418 years) recorded between 1987\u20132022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0\u20137 years), peripubertal (8\u201314 years), and postpubertal (15\u201318 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model\u2019s discriminatory ability was highest in postpubertal (AUC\u2009=\u20090.98), lower in the peripubertal age group (AUC\u2009=\u20090.91), and poor in the prepubertal age group (AUC\u2009=\u20090.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.<\/jats:p>","DOI":"10.1038\/s41746-024-01165-x","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T07:02:34Z","timestamp":1719903754000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9492-8576","authenticated-orcid":false,"given":"Donnchadh","family":"O\u2019Sullivan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5542-1599","authenticated-orcid":false,"given":"Scott","family":"Anjewierden","sequence":"additional","affiliation":[]},{"given":"Grace","family":"Greason","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9706-7900","authenticated-orcid":false,"given":"Itzhak Zachi","family":"Attia","sequence":"additional","affiliation":[]},{"given":"Francisco","family":"Lopez-Jimenez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5052-2948","authenticated-orcid":false,"given":"Paul A.","family":"Friedman","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Noseworthy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3941-0707","authenticated-orcid":false,"given":"Jason","family":"Anderson","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Kashou","sequence":"additional","affiliation":[]},{"given":"Samuel J.","family":"Asirvatham","sequence":"additional","affiliation":[]},{"given":"Benjamin W.","family":"Eidem","sequence":"additional","affiliation":[]},{"given":"Jonathan N.","family":"Johnson","sequence":"additional","affiliation":[]},{"given":"Talha","family":"Niaz","sequence":"additional","affiliation":[]},{"given":"Malini","family":"Madhavan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"1165_CR1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","volume":"394","author":"ZI Attia","year":"2019","unstructured":"Attia, Z. 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O\u2019S., S.A., G.G., P.N., J.A., S.J.A., B.W.E., J.N.J., T.N., and M.M. report no conflicts of interest. Z.A. holds an ownership interest in Xai.health and serves as an advisor to Anumana.ai and AliveCor. F. A.H. Kashou holds an executive role at The EKG Guy and has received research funding as a principal investigator or named investigator from GE HealthCare. Lopez-Jimenez is an advisor to Anumana, holds royalties as a patent beneficiary from Anumana, consults for Kento, and serves as an advisor to Novo Nordisk and Wiseacre. P. Friedman has financial interests with Anumana, Eko Health, and AliveCor.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"176"}}