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A machine learning model based on vertebral CT and clinical factors to predict osteoporotic vertebral fractures in the elderly

A machine learning model based on vertebral CT and clinical factors to predict osteoporotic vertebral fractures in the elderly

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500107790
Enrollment
Unknown
Registered
2025-08-19
Start date
2024-08-15
Completion date
Unknown
Last updated
2025-08-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Osteoporotic fractures

Interventions

Gold Standard:DXA or QCT indicates osteoporosis.
Index test:Machine learning prediction model based on clinical data and CT images

Sponsors

Huadong Hospital Affiliated to Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
60 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Diagnosis of new osteoporotic vertebral fracture by imaging data; 2. Vertebral fracture segment is T11-L2; 3. Age>=60 years old; 4. Complete vertebral CT imaging data can be obtained; 5. Have complete clinical and geriatric comprehensive assessment data required for research.

Exclusion criteria

Exclusion criteria: 1. Violent fractures, spinal metastases or primary tumors, and spinal infections; 2. History of thoracolumbar internal fixation and/or fusion surgery, history of vertebral body strengthening surgery; 3. Those who affect the delineation of the area of interest due to severe scoliosis and vertebral rotation; 4. Poor image quality does not meet the requirements of omics feature extraction.

Design outcomes

Primary

MeasureTime frame
Thoracolumbar vertebral CT;Comprehensive Geriatric Assessment;

Secondary

MeasureTime frame
Clinical data;

Countries

China

Contacts

Public ContactHong Wei

Huadong Hospital Affiliated to Fudan University

Drivyh@126.com+86 21 62483180

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026