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Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development.

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AI-enabled electrocardiograms (ECGs) can predict sex in children, with accuracy increasing with pubertal development. This technology shows high potential for pediatric sex estimation using ECG data.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Pediatric Cardiology
  • Biomedical Signal Processing

Background:

  • Artificial intelligence (AI) has demonstrated efficacy in predicting patient sex from electrocardiograms (ECGs) in adults.
  • The correlation between ECG characteristics and sex hormones suggests potential applications in pediatric populations.
  • Understanding the influence of pubertal development on ECG-sex prediction is crucial for pediatric applications.

Purpose of the Study:

  • To evaluate the capability of AI-enabled ECGs to predict sex in pediatric patients.
  • To investigate the impact of pubertal development stages on the accuracy of AI-based sex prediction from ECGs.
  • To compare the performance of a de novo pediatric AI model with a transfer learning model derived from adult data.

Main Methods:

  • Development of AI models using convolutional neural networks trained on 10-second, 12-lead pediatric ECGs.
  • Training and validation datasets were established from 90,133 unique pediatric patient ECGs (age ≤18 years).
  • Subgroup analysis was conducted across prepubertal (0-7), peripubertal (8-14), and postpubertal (15-18) age groups.

Main Results:

  • The de novo pediatric AI model achieved 81% accuracy and an Area Under the Curve (AUC) of 0.91 across the entire test cohort.
  • Model performance varied by pubertal stage, with higher discriminatory ability in postpubertal (AUC=0.98) and peripubertal (AUC=0.91) children compared to prepubertal children (AUC=0.67).
  • No significant performance difference was found between the de novo and transfer learning models.

Conclusions:

  • AI-enabled ECG interpretation can accurately estimate sex in peripubertal and postpubertal children.
  • The accuracy of AI-based sex prediction from ECGs is influenced by the stage of pubertal development.
  • AI holds promise for non-invasive sex estimation in pediatric care, particularly in older children and adolescents.