AI model uses routine ECG data to track child maturity and biological development patterns

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Researchers at Wake Forest University School of Medicine developed an AI-based score that analyzes routine heart test data to measure biological development in children and adolescents. The Electrocardiographic Sex Index (ESI) captures gradual maturation changes across more than 60,000 ECGs, offering researchers a new tool when hormone or puberty data isn't available.

AI Model Transforms Routine Heart Tests Into Developmental Tracking Tool

Researchers at Wake Forest University School of Medicine have developed an AI model that extracts biological development insights from routine ECG data, potentially changing how scientists track child maturity in large-scale studies

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. The findings, published in European Heart Journal - Digital Health, introduce the Electrocardiographic Sex Index (ESI), an AI-based score that measures biological development patterns on a continuous spectrum rather than fixed categories

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Source: Newswise

Source: Newswise

Pediatric researchers have long struggled with the absence of reliable measures of pubertal stage or hormone levels in large datasets, forcing them to rely on broad classifications that fail to capture the gradual nature of how children develop. The new approach addresses this gap by analyzing standard electrocardiogram readings to track how kids grow and mature without requiring invasive testing or specialized assessments

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Analyzing Over 60,000 ECGs Reveals Predictable Maturation Patterns

The research team applied an adult-trained ESI model to 61,930 ECGs from children ages 0-18 years, drawn from the clinical archive at the University of Tennessee Health Science Center

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. Remarkably, the model was applied without retraining or recalibration, allowing investigators to directly observe how ECG features evolve relative to adult benchmarks.

The routine heart test data revealed distinct patterns across developmental stages. In early childhood, ESI values remained tightly centered with minimal differences between children. However, beginning in late childhood and becoming more pronounced through adolescence, the values diverged in opposite directions before plateauing in mid-to-late adolescence

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. These age-related trends appeared consistently across all races, and the model's accuracy steadily improved as children aged, approaching adult-level performance in older adolescents.

Source: News-Medical

Source: News-Medical

Offering a Continuous Measure of Maturity for Researchers

"One of the most exciting aspects of this work is it shows routine ECG data may contain meaningful information about biological maturation in children and adolescents," said Tolga Hayit, Ph.D., visiting researcher and the study's co-lead author

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. "ESI offers a continuous measure that may help researchers account for developmental stage when Tanner staging, a standard medical system used to describe the five stages of puberty, or hormone data, are not available."

Ibrahim Karabayir, Ph.D., assistant professor of cardiology at the Wake Forest Center for Artificial Intelligence Research and co-first author, emphasized the broader implications: "ECGs, traditionally underutilized for capturing developmental biology, can now, when coupled with state-of-the-art AI approaches, highlight their potential to uncover patterns of maturation and cardiovascular development at scale"

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Implications for Large Studies and Future Research Directions

The significance extends beyond academic interest. Many large studies involving children treat biological characteristics as simple categories, which fails to capture how bodies develop over time. ESI provides researchers working on large studies with a practical tool to account for gradual, stage-by-stage changes when information about puberty or hormone levels remains unavailable

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While the current study does not assess clinical outcomes in children, it establishes a foundation for examining how developmental maturity influences cardiovascular health, treatment response, or long-term outcomes using ECGs already collected in routine care

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. Researchers emphasize the need for longitudinal studies incorporating Tanner staging, hormone measurements, and clinical outcomes to further evaluate the biological and clinical significance of ESI in pediatric populations. This measure of maturity could reshape how scientists understand the relationship between biological development and health outcomes in adolescents, particularly as AI continues to unlock hidden patterns in medical data already being collected during routine care.

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