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Updated: Jun 25, 2026

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AI-Based Opportunistic CT Risk Assessment Using TotalSegmentator in Osteoporotic Vertebral Fractures.

Magdalena Seng1, Jakob Wasserthal1, Michael Bach1

  • 1Department of Radiology and Nuclear Medicine, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.

Journal of Imaging Informatics in Medicine
|June 23, 2026
PubMed
Summary

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This summary is machine-generated.

Automated CT scan analysis can detect osteoporotic vertebral fractures by assessing bone density and paraspinal muscles. This integrated approach improves patient identification for timely intervention.

Area of Science:

  • Radiology
  • Medical Imaging
  • Osteoporosis Research

Background:

  • Osteoporotic vertebral fractures significantly impact patient quality of life, morbidity, and mortality.
  • Routine computed tomography (CT) examinations present an opportunity for early risk stratification of these fractures.

Purpose of the Study:

  • To evaluate if fully automated analysis of lumbar CT scans, integrating vertebral fracture detection with vertebral attenuation and paraspinal muscle metrics, enhances the identification of patients with osteoporotic vertebral fractures.

Main Methods:

  • Retrospective analysis of 1209 lumbar CT examinations after exclusion of non-diagnostic scans.
  • Automated segmentation of vertebrae and paraspinal muscles using TotalSegmentator for volume and attenuation extraction.
  • In-house algorithm for vertebral fracture detection, validated against radiologist review (Genant grade ≥ 1).
Keywords:
Artificial intelligenceBone mineral densityOpportunistic screeningOsteoporosisVertebral fractures

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Main Results:

  • 56.1% of analyzed examinations showed at least one lumbar vertebral fracture.
  • Automated fracture detection achieved 90.9% accuracy at the examination level.
  • A multiparametric model combining vertebral attenuation, paraspinal muscle volume, and attenuation significantly improved fracture discrimination (AUC 0.83) compared to attenuation alone (AUC 0.73).

Conclusions:

  • Automated opportunistic analysis of routine CT scans offers a promising method for identifying patients with osteoporotic vertebral fractures.
  • Integrating vertebral fracture detection with bone and muscle biomarkers provides superior identification compared to attenuation assessment alone.
  • This approach can facilitate early risk stratification and intervention for patients at risk of osteoporotic fractures.