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

• Published in: Scientific reports Vol. 15; no. 1; pp. 20756 - 14
• Main Authors: Palm, Viktoria, Thangamani, Subasini, Budai, Bettina Katalin, Skornitzke, Stephan, Eckl, Kira, Tong, Elizabeth, Sedaghat, Sam, Heußel, Claus Peter, von Stackelberg, Oyunbileg, Engelhardt, Sandy, Kopytova, Taisiya, Norajitra, Tobias, Maier-Hein, Klaus H., Kauczor, Hans-Ulrich, Wielpütz, Mark Oliver
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 692/308/575; 692/698/1671/63; Adult; Anthropometry; Artificial intelligence; Female; Humanities and Social Sciences; Humans; Image interpretation, computer-assisted; Image processing, computer-assisted; Male; Middle Aged; multidisciplinary; Nomograms; Reproducibility of Results; Science; Science (multidisciplinary); Sex Characteristics; Sex Factors; Spine; Spine - anatomy & histology; Spine - diagnostic imaging; Thoracic Vertebrae - anatomy & histology; Thoracic Vertebrae - diagnostic imaging; Tomography, X-Ray Computed - methods; Young Adult; Image interpretation, computer-assisted; Sex characteristics; Spine; Artificial intelligence; Anthropometry; Image processing, computer-assisted
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-05091-0

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.