Multicenter validation of AI-enabled ECG for pediatric biological sex prediction

npj Digital Medicine 2026 Open access

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Authors: Donnchadh Martin O’Sullivan, Joshua Mayourian, Scott Anjewierden, Kan Liu, Zachi Itzhak Attia, Francisco Alejandro López-Jiménez, Paul A. Friedman, Tam T. Doan, Lance W. Patterson, Jennifer L. Dugan, Jonathan N. Johnson, Santiago O. Valdés
+6 more Daniel J. Penny, John K. Triedman, Jeffrey J. Kim, Talha Niaz, Shaine Alaine Morris, Malini Madhavan
DOI: 10.1038/s41746-026-02416-9
Cited by: 0
References: 13

Abstract

Biological sex is closely linked to patterns embedded within the electrocardiogram (ECG) with essential health and disease implications. We report multicenter verification of an AI-enabled ECG model to predict biological sex across pediatric development. A previously published Mayo Clinic model confirmed puberty-linked AUROC gradient during external validation at Texas Children's Hospital (pre-puberty AUROC 0.64, peri-puberty AUROC 0.84, post-puberty AUROC 0.94). This phenomenon was replicated at Boston Children's Hospital. Saliency mapping revealed established sex-related electrophysiologic patterns.

Fields of Study & Subjects

ECG Monitoring and Analysis Machine Learning in Healthcare Artificial Intelligence in Healthcare

Publication details

Journal / source
npj Digital Medicine
Volume
9
Issue
1
Publication date
26 February 2026
Language
EN