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Prediction of the risk of lumbar spine fracture in elderly patients based on deep learning from CT images

Prediction of the risk of lumbar spine fracture in elderly patients based on deep learning from CT images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000036678
Enrollment
Unknown
Registered
2020-08-24
Start date
2021-01-01
Completion date
Unknown
Last updated
2020-09-21

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

Conditions

osteoporosis

Interventions

Fracture group:None
None fracture group:None

Sponsors

Shanghai General Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to 90 Years

Inclusion criteria

Inclusion criteria: Old patients over 65

Exclusion criteria

Exclusion criteria: (1) Other causes of fracture such as tumor and trauma should be considered; (2) Other metabolic bone diseases, such as osteosclerosis, Paget's disease, rheumatoid arthritis, osteomalacia, osteogenesis imperfecta, osteosclerosis, ankylosing spondylitis, Cushing's disease, hyperprolactinemia and malabsorption syndrome; (3) Significant mental disorders (such as schizophrenia and severe depression), as well as alcohol abuse or drug use disorders; (4) Serious visual and hearing impairment and can not cooperate with the examination; (5) Serious heart, lung, renal insufficiency patients.

Design outcomes

Primary

MeasureTime frame
Bone mass;Muscle mass;—

Secondary

MeasureTime frame
Cross sectional area of bone;—

Countries

China

Contacts

Public ContactZhu Qi

Shanghai General Hospital

vickey330@126.com+86 13917791335

Outcome results

None listed

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