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NCT07474571
Evaluation of Musculoskeletal Aging and Related Disorders Via Advanced Clinical Imaging
trial testing No Interventions in Osteoarthitis in 2,000 participants. Currently enrolling.
1 December 2026
Quick facts
| Lead sponsor | Peking University People's Hospital |
|---|---|
| Status | Recruiting now |
| Study type | OBSERVATIONAL |
| Enrollment | 2,000 |
| Start date | 1 November 2005 |
| Primary completion | 1 December 2026 |
| Estimated completion | 31 December 2026 |
| Sites | 1 location across China |
Drugs / interventions tested
- No Interventions — full drug profile →
Conditions studied
- Osteoarthitis — all drugs for Osteoarthitis →
- Osteoporosis — all drugs for Osteoporosis →
Sponsor
Peking University People's Hospital
Who can join
18 and older, any sex, with Osteoarthitis or Osteoporosis. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Study Overview This clinical research focuses on the development and validation of a multimodal artificial intelligence (AI) platform designed for the automated diagnosis and precise staging of two major musculoskeletal conditions: Osteoporosis (OP) and Osteoarthritis (OA). By integrating diverse clinical imaging data, the study aims to provide a more objective and standardized approach to assessing bone and joint degeneration. Technological Core: Intelligent Staging Traditional diagnosis often relies on manual interpretation, which can lead to inter-observer variability. This study employs deep learning and multimodal imaging to: For Osteoporosis: Automatically quantify bone mineral density and micro-architectural changes to determine the stage of bone loss and evaluate fracture risk. For Osteoarthritis: Identify subtle radiological markers such as joint space narrowing and osteophyte formation to categorize the severity of joint degeneration according to international staging standards (e.g., Kellgren-Lawrence scale). Why This Matters Early Intervention: By identifying early-stage changes in bone density and joint integrity, clinicians can implement preventive treatments before significant disability occurs. Standardized Care: The intelligent diagnostic model provides a "digital second opinion," ensuring consistent staging across different healthcare settings. Efficiency: The automated workflow reduces the workload of radiologists while maintaining high diagnostic accuracy. Ethical Compliance The study is conducted at Peking University People's Hospital under the supervision of the Institutional Review Board (Approval No. 2026PHB097-001). It strictly adheres to international ethical standards, including the Declaration of Helsinki and Good Clinical Practice (GCP) guidelines, to ensure patient data privacy and safety.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07474571 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Peking University People's Hospital
- Last refreshed: 19 March 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07474571.
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