CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age40+
SponsorXin Fan
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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 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.
Trial design
Design model
Case-control
Time perspective
Retrospective
Treatments tested in this trial
No Intervention (Observational Study)
Other interventionThis 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.
Treatment groups
276 Participants
are divided into 2 treatment groupsGroup A: Acute Osteoporotic Vertebral Compression Fracture Group1 intervention
Group B: Chronic Osteoporotic Vertebral Compression Fracture Group1 intervention
Trial outcomes
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).
Time frame
At the time of image analysisSponsors and contacts
Click on the lead sponsor to view all of their trials.
Xin Fan
Lead sponsor
First Affiliated Hospital of Chongqing Medical University
Sponsor institution
Source ClinicalTrials.gov
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