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Using MRI-based deep learning algorithms for accurate diagnosis of early-stage spinal tuberculosis and fresh vertebral compression fraMRIures.

Using MRI-based deep learning algorithms for accurate diagnosis of early-stage spinal tuberculosis and fresh vertebral compression fraMRIures.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400080950
Enrollment
Unknown
Registered
2024-02-19
Start date
2024-02-19
Completion date
Unknown
Last updated
2024-02-26

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

Conditions

Spinal tuberculosis, spinal osteoporosis, compression fractures

Interventions

Spinal tuberculosis group/fresh osteoporosis compression fracture group:None

Sponsors

The First Affiliated Hospital of Chongqing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. Thoracic and lumbar spine MRI examination with complete imaging data; 2. Pathological examination or culture examination confirmed spinal tuberculosis or vertebral compression fracture; 3. Early spinal tuberculosis without severe spinal deformity

Exclusion criteria

Exclusion criteria: 1. Patients with other pathological fractures such as spinal tumors, 2. Severe spinal deformities, fluid abscesses, burst fractures, etc., 3. History of previous spinal surgery and implant history.

Design outcomes

Primary

MeasureTime frame
Deep learning models;ROC curve;AUC;—

Countries

China

Contacts

Public ContactLiu Bo

The First Affiliated Hospital of Chongqing Medical University

boliu@hospital.cqmu.edu.cn+86 139 9606 5698

Outcome results

None listed

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