Artificial Intelligence Enhanced Electrocardiogram Analysis for Age and Sex Classification in Youth - Takeaways - MDSpire

Artificial Intelligence Enhanced Electrocardiogram Analysis for Age and Sex Classification in Youth

  • By

  • Honggen Zhang

  • Mohammad Zaeri-Amirani

  • Mojtaba Abolfazli

  • Narayana P. Santhanam

  • June Zhang

  • Anders Høst-Madsen

  • Chieko Kimata

  • James C. Perry

  • Andras Bratincsak

  • February 18, 2026

  • 0 min

Share

  • 1

    AI-enhanced ECG analysis can improve the accuracy of age and sex classification in pediatric populations.

  • 2

    The study utilized a curated cohort of 29,408 ECGs from children aged 1 day to 21 years with no known heart conditions.

  • 3

    Machine learning models were developed to leverage complex ECG data for identifying demographic factors in pediatric patients.

  • 4

    AI models have shown promise in detecting various heart conditions in children, but age- and sex-specific standards are lacking.

  • 5

    Establishing normative ECG standards for children is crucial for effective screening of rare heart conditions and congenital defects.

Original Source(s)

Artificial Intelligence Enhanced Electrocardiogram Analysis for Age and Sex Classification in Youth

  • by Honggen Zhang, Mohammad Zaeri-Amirani, Mojtaba Abolfazli, Narayana P. Santhanam, June Zhang, Anders Høst-Madsen, Chieko Kimata, James C. Perry, Andras Bratincsak

  • February 18, 2026

Related Content

Case Report: Cardiac metastasis from urothelial carcinoma with predominant squamous differentiation in a young woman: first reported case treated with enfortumab vedotin plus pembrolizumab

AD Not Linked to Early CV Risk Markers

Repeated assessments from childhood to early adulthood did not identify consistent cardiometabolic risk patterns by disease activity or severity.

Pulse Oximetry, Skin Pigmentation, and Occult Hypoxemia in Pediatric Cardiac Critical Care