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@ARTICLE{Palm:302867,
author = {V. Palm and S. Thangamani and B. K. Budai and S. Skornitzke
and K. Eckl and E. Tong and S. Sedaghat and C. P. Heußel
and O. von Stackelberg and S. Engelhardt and T. Kopytova$^*$
and T. Norajitra$^*$ and K. Maier-Hein$^*$ and H.-U. Kauczor
and M. O. Wielpütz},
title = {{AI}-based {CT} assessment of 3117 vertebrae reveals
significant sex-specific vertebral height differences.},
journal = {Scientific reports},
volume = {15},
number = {1},
issn = {2045-2322},
address = {[London]},
publisher = {Springer Nature},
reportid = {DKFZ-2025-01407},
pages = {20756},
year = {2025},
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.},
keywords = {Humans / Adult / Male / Female / Middle Aged / Tomography,
X-Ray Computed: methods / Young Adult / Sex Characteristics
/ Thoracic Vertebrae: diagnostic imaging / Thoracic
Vertebrae: anatomy $\&$ histology / Sex Factors / Spine:
diagnostic imaging / Spine: anatomy $\&$ histology /
Nomograms / Reproducibility of Results / Anthropometry
(Other) / Artificial intelligence (Other) / Image
interpretation, computer-assisted (Other) / Image
processing, computer-assisted (Other) / Sex characteristics
(Other) / Spine (Other)},
cin = {E230},
ddc = {600},
cid = {I:(DE-He78)E230-20160331},
pnm = {315 - Bildgebung und Radioonkologie (POF4-315)},
pid = {G:(DE-HGF)POF4-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:40595983},
pmc = {pmc:PMC12218285},
doi = {10.1038/s41598-025-05091-0},
url = {https://inrepo02.dkfz.de/record/302867},
}