CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures

NCT07306858 | Not Yet Recruiting DMC

• 1 other identifier: KX2025-KYC1056-01
• Study Type: observational
• Target: 276
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Osteoporotic Vertebral Compression Fractures

Keywords

Osteoporotic vertebral compression fracture; Deep learning

Outcome Measures

Primary Outcomes (1)

• 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).

  At the time of image analysis

Study Arms (2)

Acute Osteoporotic Vertebral Compression Fracture Group

Patients diagnosed with acute osteoporotic vertebral compression fractures based on clinical assessment and imaging findings.

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.

Other: No Intervention (Observational Study)

Interventions

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.

Acute Osteoporotic Vertebral Compression Fracture Group; Chronic Osteoporotic Vertebral Compression Fracture Group

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Xin Fan: lead

Study Sites (1)

The First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, 400016, China

Location

MeSH Terms

Interventions

Observation

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 15, 2025
• First Posted: December 29, 2025
• Study Start: December 16, 2025
• Primary Completion: December 29, 2025
• Study Completion: January 15, 2026
• Last Updated: December 31, 2025

Record last verified: 2025-12

Locations

CN (1)