Evaluation of Musculoskeletal Aging and Related Disorders Via Advanced Clinical Imaging

NCT07474571 | Recruiting

• 1 other identifier: 2026PHB097-001
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Osteoarthitis; Osteoporosis

Keywords

artificial intelligence; osteoarthitis; diagnosis; machine learning; osteoporosis; clinical images

Outcome Measures

Primary Outcomes (1)

• Bone age status

  Bone mineral density and osteoarthitis

  From enrollment to the initial treatment at 2 years

Study Arms (6)

Peking University People's Hospital cohort 1

participants with osteoarthritis

Other: No Interventions

Peking University People's Hospital cohort 2

participants with osteoporosis

Other: No Interventions

Sun yat-sen memorial hospital 1

participants with osteoarthritis

Other: No Interventions

Sun yat-sen memorial hospital 2

participants with osteoporosis

Other: No Interventions

Peking University People's Hospital cohort 3

participants without osteoporosis and osteoarthritis

Other: No Interventions

Sun yat-sen memorial hospital 3

participants without osteoporosis and osteoarthritis

Other: No Interventions

Interventions

No Interventions OTHER

No Interventions

Peking University People's Hospital cohort 1; Peking University People's Hospital cohort 2; Peking University People's Hospital cohort 3; Sun yat-sen memorial hospital 1; Sun yat-sen memorial hospital 2; Sun yat-sen memorial hospital 3

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Study Population DescriptionThe study population consists of adult patients seeking medical consultation or treatment at the Department of Orthopaedic Trauma, Peking University People's Hospital. This population represents a diverse clinical spectrum of musculoskeletal aging, ranging from healthy skeletal status to various stages of Osteoporosis (OP) and Osteoarthritis (OA).The research employs a bidirectional cohort structure:Retrospective Cohort: Includes approximately 1,500 patients treated between November 2005 and November 2025, primarily used for model training and internal algorithm validation.Prospective Cohort: Includes at least 500 patients recruited starting from December 2025, used for independent external validation of the model's diagnostic accuracy and staging robustness in a real-world clinical setting.Participants are characterized by their availability of multimodal data, including radiological images (X-ray, CT, MRI) and clinical laboratory indicators (e.g., bone tur

You may qualify if:

• Age: Adults aged at least 18 years.
• Gender: No gender restrictions; both male and female participants are eligible.
• Imaging Data: Participants must have completed relevant clinical imaging scans of skeletal sites, including but not limited to X-ray, CT (plain scan), or MRI (plain scan).
• Anatomical Integrity: The skeletal structure of the target area must be free from congenital or acquired deformities.
• Absence of Implants: No internal fixation materials or orthopedic implants in the skeletal areas being assessed.

You may not qualify if:

• Pathological History: Patients with a history of prior pathological fractures.
• Malignancy: Patients seeking treatment or diagnosed with bone tumors or other systemic malignancies.
• Medication History: Patients with a history of long-term steroid use, which may significantly affect bone density and joint structure.
• Recent Treatment: Patients who have undergone radiotherapy or chemotherapy within the past six months.
• Data Quality: Patients whose imaging data is of insufficient quality for AI analysis or lacks clear clinical diagnostic "gold standard" references (e.g., missing DXA results for osteoporosis staging).

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Unknown Facility

Beijing, China

RECRUITING

MeSH Terms

Conditions

Osteoporosis; Disease

Condition Hierarchy (Ancestors)

Bone Diseases, Metabolic; Bone Diseases; Musculoskeletal Diseases; Metabolic Diseases; Nutritional and Metabolic Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Yuhui Kou, M.D

CONTACT

13146213332; yuhuikou@bjmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Fellow, Assistant to the Department of Orthopaedic Trauma

Study Record Dates

• First Submitted: March 11, 2026
• First Posted: March 16, 2026
• Study Start: November 1, 2005
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: March 19, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)