Osteoporotic Vertebral Compression FracturesCT-DL-OVCFNCT07306858
CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
An observational study of Osteoporotic Vertebral Compression Fractures, run by Xin Fan at 1 site in China.
Trial summary
Osteoporotic vertebral compression fractures are common in older adults and may present as either acute or chronic fractures. Correctly distinguishing acute from chronic fractures is clinically important because treatment strategies and management decisions differ depending on fracture chronicity. Read more about this study
- Who is it for?
- Ages 40 years and older
- Conditions the study is for
- Osteoporotic Vertebral Compression Fractures
- How many people
- 276 participants
- Where
- 1 siteChina
- Study dates
- 16 Dec 2025to 15 Jan 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult patients aged 40 years or older who were diagnosed with osteoporotic vertebral compression fractures and underwent CT and MRI examinations at the participating centers.
Who may be able to join
- Patients diagnosed with osteoporotic vertebral compression fractures.
- 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.
Who cannot join
- Vertebral compression fractures caused by infection or malignancy.
- Presence of foreign materials, including bone cement or metallic hardware.
- Poor image quality or significant imaging artifacts that affect analysis.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, 400016
Xin Fan, +86 23 89011876, 202770@hospital.cqmu.edu.cn
202770@hospital.cqmu.edu.cn
About this study
Official title: A Deep Learning Model Based on CT Images for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
Osteoporotic vertebral compression fractures are common in older adults and may present as either acute or chronic fractures. Correctly distinguishing acute from chronic fractures is clinically important because treatment strategies and management decisions differ depending on fracture chronicity. However, differentiating acute and chronic osteoporotic vertebral compression fractures based on imaging findings alone can be challenging in routine clinical practice.
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.
Read the detailed description
This study is a retrospective, multicenter observational study designed to develop and evaluate a deep learning-based system for differentiating acute and chronic osteoporotic vertebral compression fractures using computed tomography (CT) images.
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.
- Being tested
- No Intervention (Observational Study)
- Keywords
- Osteoporotic vertebral compression fracture, Deep learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Intervention (Observational Study)Other
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.
| Study group | Role | What they receive |
|---|---|---|
| Acute Osteoporotic Vertebral Compression Fracture Group | Patients diagnosed with acute osteoporotic vertebral compression fractures based on clinical assessment and imaging findings. Receives: Other: No Intervention (Observational Study) | |
| Chronic Osteoporotic Vertebral Compression Fracture Group | Patients diagnosed with chronic osteoporotic vertebral compression fractures based on clinical assessment and imaging findings. Receives: Other: No Intervention (Observational Study) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the deep learning model for differentiating acute and chronic osteoporotic vertebral compression fractures
The diagnostic performance of the deep learning model in differentiating acute and chronic osteoporotic vertebral compression fractures based on CT images, evaluated using the area under the receiver operating characteristic curve (AUC).
Measured: At the time of image analysis
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Dec 2025
- Study start16 Dec 2025 (estimated)
- Primary completion29 Dec 2025 (estimated)
- Study completion15 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Osteoporotic Vertebral Compression FracturesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 276 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xin Fan
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Xin Fan, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07306858
- Sponsor's protocol ID
- KX2025-KYC1056-01
- Acronym
- CT-DL-OVCF
- Status last verified
- Dec 2025
- First submitted
- 15 Dec 2025
- Last updated
- 31 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: A final decision on sharing individual participant data has not been made at the time of registration. Potential data sharing will be considered in accordance with institutional review board approval, patient privacy protection, and relevant data governance policies.
Study at a glance
From the registry record, last updated by the sponsor on 31 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning Model Based on CT Images for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
- ClinicalTrials.gov ID
- NCT07306858
- Acronym / sponsor ID
- CT-DL-OVCF / KX2025-KYC1056-01
- Keywords
- Osteoporotic vertebral compression fracture, Deep learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 276 participants (estimated)
- Lead sponsor
- Xin FanUniversity, hospital or other
- Study start
- 16 Dec 2025 (estimated)
- Primary completion
- 29 Dec 2025 (estimated)
- Study completion
- 15 Jan 2026 (estimated)
- First posted
- 29 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Osteoporotic vertebral compression fractures are common in older adults and may present as either acute or chronic fractures. Correctly distinguishing acute from chronic fractures is clinically important because treatment strategies and management decisions differ depending on fracture chronicity. However, differentiating acute and chronic osteoporotic vertebral compression fractures based on imaging findings alone can be challenging in routine clinical practice.
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
This study is a retrospective, multicenter observational study designed to develop and evaluate a deep learning-based system for differentiating acute and chronic osteoporotic vertebral compression fractures using computed tomography (CT) images.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Acute Osteoporotic Vertebral Compression Fracture Group | – | Other: No Intervention (Observational Study)Patients diagnosed with acute osteoporotic vertebral compression fractures based on clinical assessment and imaging findings. |
| Chronic Osteoporotic Vertebral Compression Fracture Group | – | Other: No Intervention (Observational Study)Patients diagnosed with chronic osteoporotic vertebral compression fractures based on clinical assessment and imaging findings. |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients aged 40 years or older who were diagnosed with osteoporotic vertebral compression fractures and underwent CT and MRI examinations at the participating centers.
Key inclusion criteria (5 listed)
- Patients diagnosed with osteoporotic vertebral compression fractures.
- 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.
Key exclusion criteria (3 listed)
- Vertebral compression fractures caused by infection or malignancy.
- Presence of foreign materials, including bone cement or metallic hardware.
- Poor image quality or significant imaging artifacts that affect analysis.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Other | Acute Osteoporotic Vertebral Compression Fracture Group, Chronic Osteoporotic Vertebral Compression Fracture Group |
Primary outcome
Diagnostic performance of the deep learning model for differentiating acute and chronic osteoporotic vertebral compression fractures
Time frame: At the time of image analysis
The diagnostic performance of the deep learning model in differentiating acute and chronic osteoporotic vertebral compression fractures based on CT images, evaluated using the area under the receiver operating characteristic curve (AUC).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Dec 2025.
- Individual participant dataSharing planned: Undecided. A final decision on sharing individual participant data has not been made at the time of registration. Potential data sharing will be considered in accordance with institutional review board approval, patient privacy protection, and relevant data governance policies.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The First Affiliated Hospital of Chongqing Medical University
Chongqing, China
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Osteoporotic Vertebral Compression Fractures, run by Xin Fan at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 31 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of the deep learning model for differentiating acute and chronic osteoporotic vertebral compression fractures (time frame: At the time of image analysis). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Dec 2025.