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<p>Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height prediction models, aiding in diagnosing spinal conditions like compression fractures and supporting individualized, sex-specific medicine. In this study an AI-based CT-imaging spine analysis of 262 subjects (mean age 32.36 years, range 20–54 years) was conducted, including a total of 3117 vertebrae, to assess sex-associated anatomical variations. Automated segmentations provided anterior, central, and posterior vertebral heights. Regression analysis with a cubic spline linear mixed-effects model was adapted to age, sex, and spinal segments. Measurement reliability was confirmed by two readers with an intraclass correlation coefficient (ICC) of 0.94–0.98. Female vertebral heights were consistently smaller than males (<i>p</i> &lt; 0.05). The largest differences were found in the upper thoracic spine (T1–T6), with mean differences of 7.9–9.0%. Specifically, T1 and T2 showed differences of 8.6% and 9.0%, respectively. The strongest height increase between consecutive vertebrae was observed from T9 to L1 (mean slope of 1.46; 6.63% for females and 1.53; 6.48% for males). This study highlights significant sex-based differences in vertebral heights, resulting in sex-adapted nomograms that can enhance diagnostic accuracy and support individualized patient assessments.</p>

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AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Viktoria Palm,
  • Subasini Thangamani,
  • Bettina Katalin Budai,
  • Stephan Skornitzke,
  • Kira Eckl,
  • Elizabeth Tong,
  • Sam Sedaghat,
  • Claus Peter Heußel,
  • Oyunbileg von Stackelberg,
  • Sandy Engelhardt,
  • Taisiya Kopytova,
  • Tobias Norajitra,
  • Klaus H. Maier-Hein,
  • Hans-Ulrich Kauczor,
  • Mark Oliver Wielpütz

摘要

Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height prediction models, aiding in diagnosing spinal conditions like compression fractures and supporting individualized, sex-specific medicine. In this study an AI-based CT-imaging spine analysis of 262 subjects (mean age 32.36 years, range 20–54 years) was conducted, including a total of 3117 vertebrae, to assess sex-associated anatomical variations. Automated segmentations provided anterior, central, and posterior vertebral heights. Regression analysis with a cubic spline linear mixed-effects model was adapted to age, sex, and spinal segments. Measurement reliability was confirmed by two readers with an intraclass correlation coefficient (ICC) of 0.94–0.98. Female vertebral heights were consistently smaller than males (p < 0.05). The largest differences were found in the upper thoracic spine (T1–T6), with mean differences of 7.9–9.0%. Specifically, T1 and T2 showed differences of 8.6% and 9.0%, respectively. The strongest height increase between consecutive vertebrae was observed from T9 to L1 (mean slope of 1.46; 6.63% for females and 1.53; 6.48% for males). This study highlights significant sex-based differences in vertebral heights, resulting in sex-adapted nomograms that can enhance diagnostic accuracy and support individualized patient assessments.