Buchumschlag

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

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 l...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Palm, Viktoria (VerfasserIn) , Thangamani, Subasini (VerfasserIn) , Budai, Bettina Katalin (VerfasserIn) , Skornitzke, Stephan (VerfasserIn) , Eckl, Kira (VerfasserIn) , Tong, Elizabeth (VerfasserIn) , Sedaghat, Sam (VerfasserIn) , Heußel, Claus Peter (VerfasserIn) , Stackelberg, Oyunbileg von (VerfasserIn) , Engelhardt, Sandy (VerfasserIn) , Kopytova, Taisiya (VerfasserIn) , Norajitra, Tobias (VerfasserIn) , Maier-Hein, Klaus H. (VerfasserIn) , Kauczor, Hans-Ulrich (VerfasserIn) , Wielpütz, Mark Oliver (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 01 July 2025
In: Scientific reports
Year: 2025, Jahrgang: 15, Pages: 1-14
ISSN:2045-2322
DOI:10.1038/s41598-025-05091-0
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-025-05091-0
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-025-05091-0
Volltext
Verfasserangaben: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
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 12.01.2026
Beschreibung:Online Resource
ISSN:2045-2322
DOI:10.1038/s41598-025-05091-0

Ähnliche Einträge