Abstract
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.