CT-Based Deep Learning 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.

NCT Number	NCT07306858
Lead Sponsor	Xin Fan
Conditions	Osteoporotic Vertebral Compression Fractures
Enrollment	276 participants
Start Date	2025-12-16
Primary Completion	2025-12-29 (estimated)
Study Completion	2026-01-15 (estimated)
Updated on ClinicalTrials.gov	2025-12-31