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Application of deep learning and machine learning in spinal imaging research

Application of deep learning in spinal surgery research

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300075053
Enrollment
Unknown
Registered
2023-08-23
Start date
2023-08-25
Completion date
Unknown
Last updated
2023-08-28

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

Conditions

Diseases of spine

Interventions

Gold Standard:Gold Standard: 1. Magnetic resonance imaging of patients, especially T2 and FS
2. Computer tomography of patients
3. Pathological and immunohistochemistry results, etc
Index test:Based on Python, the deep learning models were developed, meanwhile, the classify and segment of different datasets within the framework of the deep learning model, and the models's perform

Sponsors

The Affiliated Hospital of Qingdao University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (1) Specific public databases (2) Patients who undergo corresponding imaging tests or undergo specific surgery (3) Have complete imaging data, clinical and follow-up data

Exclusion criteria

Exclusion criteria: (1) Patients who have undergone surgery at the target site previously (2) Incomplete data, or insufficient follow-up time (3) Serious systemic diseases, such as arachnoiditis, ankylosing spondylitis, etc

Design outcomes

Primary

MeasureTime frame
Model Effectiveness;Accuracy ;Sensitivity, SE;Specificity, SP;Negative predictive value, NPV,PV-;

Secondary

MeasureTime frame
Positive predicative value, PPV, PV+;

Countries

China

Contacts

Public ContactXuexiao Ma

The Affiliated Hospital of Qingdao University

maxuexiaospinal@163.com+86 186 6180 7895

Outcome results

None listed

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