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A deep learning framework for diagnosis periprosthetic joint infections using X-ray images

A deep learning framework for diagnosis periprosthetic joint infections using X-ray images

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200060469
Enrollment
Unknown
Registered
2022-06-02
Start date
2022-06-02
Completion date
Unknown
Last updated
2024-05-21

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

Conditions

hip and knee joint disease

Interventions

Sponsors

Department of Orthopedics, West China Hospital, Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: Patients undergoing hip or knee revision surgery in the joint group of our department from January 2008 to January 2022.

Exclusion criteria

Exclusion criteria: 1. Acute periprosthesis infection (PJI, <3 months after talar joint replacement) or acute hematogenous PJI; 2. Received antibiotic therapy within 2 weeks prior to laboratory or radiological evaluation; 3. Any resurgery of the joint (e.g. removal of prostheses and placement of spacers); 4. The clinical diagnosis is ultimately unclear.

Design outcomes

Primary

MeasureTime frame
X-ray examination;Sensitivity;The area under the curve of the subject's operating characteristic curve;Specificity;Accuracy;

Countries

China

Contacts

Public ContactShen Bin

West China Hospital, Sichuan University

shenbin_1971@163.com+86 18280214721

Outcome results

None listed

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