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Application of artificial intelligence technology in osteoporotic vertebral compression fractures

Prediction of residual distal lumbosacral pain after percutaneous kyphoplasty for osteoporotic vertebral compression fractures based on artificial intelligence technology

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400093991
Enrollment
Unknown
Registered
2024-12-16
Start date
2024-12-24
Completion date
Unknown
Last updated
2025-01-06

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

Conditions

Osteoporotic vertebral compression fractures

Interventions

Training Group:None
Testing Group:None

Sponsors

Second Affiliated Hospital of Soochow University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
50 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1. Diagnosed single segment thoracolumbar osteoporotic vertebral compression fracture (T10-L2) with clear diagnosis; 2. All patients have no symptoms of nerve or spinal cord compression; 3. All patients underwent percutaneous kyphoplasty; 4. Preoperative pain VAS score is greater than or equal to 5.

Exclusion criteria

Exclusion criteria: Chest and lumbar spine fractures caused by serious injuries such as car accidents or falls from heights. Spinal tumors, spinal tuberculosis.

Design outcomes

Primary

MeasureTime frame
VAS score for distal lumbosacral pain;—

Countries

China

Contacts

Public ContactZhang Yingzi

Second Affiliated Hospital of Soochow University

fancoolin@163.com+86 139 1310 6378

Outcome results

None listed

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