CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
NCT ID: NCT07306858
Last Updated: 2025-12-31
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
276 participants
OBSERVATIONAL
2025-12-16
2026-01-15
Brief Summary
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This retrospective study aims to develop an intelligent diagnostic system based on computed tomography (CT) images to differentiate acute and chronic osteoporotic vertebral compression fractures. Clinical and imaging data from patients diagnosed with osteoporotic vertebral compression fractures will be collected from the First Affiliated Hospital of Chongqing Medical University and an additional medical center. A deep learning model will be trained to automatically analyze CT images and classify fractures as acute or chronic.
The results of this study may help improve the accuracy and efficiency of fracture chronicity assessment using CT images and provide supportive information for clinical decision-making regarding treatment selection in patients with osteoporotic vertebral compression fractures.
Detailed Description
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Patients diagnosed with osteoporotic vertebral compression fractures who underwent both CT and magnetic resonance imaging (MRI) examinations will be retrospectively collected from the First Affiliated Hospital of Chongqing Medical University and one additional medical center between January 2023 and September 2025. Clinical data, including age, sex, and dual-energy X-ray absorptiometry (DXA) results, as well as complete DICOM-format CT and MRI images, will be collected. The interval between CT and MRI examinations must be less than two weeks. Patients with pathological fractures caused by infection or tumor, the presence of foreign materials such as bone cement or metallic hardware, or poor image quality with significant artifacts will be excluded.
The study workflow includes data collection, model development, performance evaluation, and model interpretability analysis. Multiple deep learning segmentation models, including U-Net, U-Mamba, and UNETR++, will first be evaluated for vertebral body segmentation performance. Based on the optimal segmentation results, classification models such as VGG-16, DenseNet-121, Vision Transformer (ViT), and Transformer-based architectures will be trained to differentiate acute and chronic compression fractures. The best-performing model will be selected to construct the final classification system.
Model performance for segmentation tasks will be assessed using Dice similarity coefficient and loss values. Classification performance will be evaluated in an external validation dataset using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Receiver operating characteristic curves and confusion matrices will be generated to visualize model performance.
To improve model interpretability, gradient-weighted class activation mapping (Grad-CAM) will be applied to generate heatmaps highlighting image regions that contribute most to model predictions. These heatmaps will be overlaid on CT images to visually demonstrate how the model differentiates acute and chronic osteoporotic vertebral compression fractures.
Based on a predefined sample size calculation assuming a sensitivity of 0.90, a significance level of 0.05, and an allowable error of 0.05, a total of 276 patients (138 acute and 138 chronic cases) are expected to be included in this study.
Conditions
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Keywords
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Acute Osteoporotic Vertebral Compression Fracture Group
Patients diagnosed with acute osteoporotic vertebral compression fractures based on clinical assessment and imaging findings.
No Intervention (Observational Study)
This is a retrospective observational study. No therapeutic, diagnostic, or preventive intervention is assigned as part of the study. All analyses are based on previously acquired clinical and imaging data.
Chronic Osteoporotic Vertebral Compression Fracture Group
Patients diagnosed with chronic osteoporotic vertebral compression fractures based on clinical assessment and imaging findings.
No Intervention (Observational Study)
This is a retrospective observational study. No therapeutic, diagnostic, or preventive intervention is assigned as part of the study. All analyses are based on previously acquired clinical and imaging data.
Interventions
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No Intervention (Observational Study)
This is a retrospective observational study. No therapeutic, diagnostic, or preventive intervention is assigned as part of the study. All analyses are based on previously acquired clinical and imaging data.
Eligibility Criteria
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Inclusion Criteria
* Patients who underwent both CT and MRI examinations of the spine, with an interval of less than 2 weeks between examinations.
* Availability of complete CT and MRI imaging data in DICOM format.
* Availability of complete clinical information, including age, sex, and dual-energy X-ray absorptiometry (DXA) results.
* Age 50 years or older at the time of imaging.
Exclusion Criteria
* Presence of foreign materials, including bone cement or metallic hardware.
* Poor image quality or significant imaging artifacts that affect analysis.
40 Years
ALL
No
Sponsors
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Xin Fan
OTHER
Responsible Party
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Xin Fan
Professor
Locations
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The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China
Countries
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Facility Contacts
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Xin Fan
Role: primary
Role: backup
Other Identifiers
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KX2025-KYC1056-01
Identifier Type: -
Identifier Source: org_study_id