Using imaging and artificial intelligence to stage osteoporosis and osteoarthritis

Trial details

Who runs it

Peking University People's Hospital

Trial code

NCT07474571

Record updated

19 March 2026

Original 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.Read full original descriptionShow less

A Comprehensive Evaluation of Musculoskeletal Aging and Degenerative Pathologies Using Multi-modal Clinical Imaging and Quantitative Analysis.

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.

Inclusion: 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.

Exclusion: Pathological History: Patients with a history of prior pathological fractures; Recent Treatment: Patients who have undergone radiotherapy or chemotherapy within the past six months; 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; 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).

ClinicalTrials.gov NCT07474571