Evidence map›Paper›PMID 40595983›Full record

ArticleScientific reports2025

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

Viktoria Palm, Subasini Thangamani, Bettina Katalin Budai, Stephan Skornitzke, Kira Eckl, Elizabeth Tong, Sam Sedaghat, Claus Peter Heußel, Oyunbileg von Stackelberg, Sandy Engelhardt and 5 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Viktoria PalmClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany. Viktoria.palm@med.uni-heidelberg.de.
Subasini ThangamaniClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Bettina Katalin BudaiClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Stephan SkornitzkeClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Kira EcklClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Elizabeth TongClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Sam SedaghatClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Claus Peter HeußelClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Oyunbileg von StackelbergClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Sandy EngelhardtDepartment of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany.
Taisiya KopytovaDepartment of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
Tobias NorajitraDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
Klaus H Maier-HeinDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
Hans-Ulrich KauczorClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Mark Oliver WielpützClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

SpineThoracic VertebraeTomography, X-Ray ComputedAdultFemaleHumansMaleMiddle AgedNomogramsReproducibility of ResultsSex CharacteristicsSex FactorsYoung AdultAnthropometryArtificial intelligenceImage interpretation, computer-assistedImage processing, computer-assistedSex characteristicsSpine

Identifiers

PMID40595983
PMCPMC12218285

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.