OsteoarthitisNCT07474571
Evaluation of Musculoskeletal Aging and Related Disorders Via Advanced Clinical Imaging
An observational study of Osteoarthitis and Osteoporosis, run by Peking University People's Hospital at 1 site in China.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Osteoarthitis, Osteoporosis
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2005to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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
Who may be able to join
- 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.
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Study site
Beijing
Yuhui Kou, M.D, 13146213332, yuhuikou@bjmu.edu.cn
About this study
Official title: 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
Read more
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.
Read the detailed description
- Research Rationale and Goals Musculoskeletal aging often presents as a complex interplay between Osteoporosis (OP) and Osteoarthritis (OA). Despite their prevalence, current diagnostic workflows frequently treat these conditions in isolation, often relying on manual radiological staging that is prone to inter-observer variability. This study aims to develop and validate a multimodal artificial intelligence (AI) platform capable of simultaneous detection and precise disease staging for both OP and OA. By integrating diverse data sources-including clinical laboratory markers, patient history, and multiple imaging modalities (X-ray, CT, and MRI)-the project seeks to provide a holistic and objective assessment of skeletal health.
- Study Design and Population The research employs a bidirectional observational cohort study design. Retrospective Cohort (Model Development): Data will be collected from approximately 1,500 patients who visited the Peking University People's Hospital (PKUPH) between November 2005 and November 2025. Prospective Cohort (External Validation): At least 500 new participants will be recruited starting from December 2025 to test the model's performance in a real-world clinical setting. The study targets adults aged 18 to 90 who have completed relevant musculoskeletal imaging scans.
- Methodology and AI PipelineThe study is divided into three strategic phases:Phase I: Multimodal Data Integration: Collection of de-identified imaging (X-ray/CT/MR), laboratory indices (e.g., bone turnover markers, calcium-phosphorus metabolism), and clinical demographics (Age, BMI, medical history).Phase II: Intelligent Diagnostic Staging: Leveraging Convolutional Neural Networks (CNN) for image feature extraction and machine learning algorithms (e.g., XGBoost, SVM) for clinical feature fusion.For Osteoporosis: The model will categorize bone health stages (Normal, Osteopenia, Osteoporosis) using DXA T-scores as the gold standard.For Osteoarthritis: The system will automate grading based on the Kellgren-Lawrence (KL) scale, identifying joint space narrowing and osteophytic progression.Phase III: Validation and Explainability: Internal cross-validation and independent external testing using the prospective cohort. SHAP (Shapley Additive Explanations) analysis will be applied to quantify the contribution of each modality to the final diagnostic decision, ensuring clinical transparency.
- Outcome Measures The primary outcome is the diagnostic accuracy (AUC, Sensitivity, Specificity) of the AI model for both conditions. Secondary outcomes focus on long-term clinical utility, including the incidence of new fragility fractures and changes in functional scores (e.g., VAS or OKS) during the follow-up periods (6, 12, 24, and 36 months).
- Ethical Oversight and Data Safety The study is conducted at Peking University People's Hospital under the approval of the Institutional Review Board (Approval No. 2026PHB097-001). It adheres to GCP principles and the Declaration of Helsinki. All imaging and clinical data are strictly de-identified (anonymized) before being entered into the secure, encrypted research database to protect patient privacy.
- Conditions
- Osteoarthitis, Osteoporosis
- Being tested
- No Interventions
- Keywords
- artificial intelligence, osteoarthitis, diagnosis, machine learning, osteoporosis, clinical images
- Medical subject headings
- Osteoporosis, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionsOther
No Interventions
| Study group | Role | What they receive |
|---|---|---|
| Peking University People's Hospital cohort 1 | participants with osteoarthritis Receives: Other: No Interventions | |
| Peking University People's Hospital cohort 2 | participants with osteoporosis Receives: Other: No Interventions | |
| Sun yat-sen memorial hospital 1 | participants with osteoarthritis Receives: Other: No Interventions | |
| Sun yat-sen memorial hospital 2 | participants with osteoporosis Receives: Other: No Interventions | |
| Peking University People's Hospital cohort 3 | participants without osteoporosis and osteoarthritis Receives: Other: No Interventions | |
| Sun yat-sen memorial hospital 3 | participants without osteoporosis and osteoarthritis Receives: Other: No Interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Bone age status
Bone mineral density and osteoarthitis
Measured: From enrollment to the initial treatment at 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Mar 2026
- Study start1 Nov 2005
- Primary completion1 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OsteoarthitisStudies of the same condition, open and under way.
- More trials run by Peking University People's HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yuhui Kou, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07474571
- Sponsor's protocol ID
- 2026PHB097-001
- Status last verified
- Mar 2026
- First submitted
- 11 Mar 2026
- Last updated
- 19 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Privacy and Confidentiality Risks: The study involves sensitive clinical imaging (X-ray, CT, MRI) and laboratory data from patients at Peking University People's Hospital. Even after de-identification, there is a risk that high-resolution medical images could be re-identified, violating patient privacy agreements .Institutional Data Policy: The protocol states that data processing and export are restricted to computers registered within the hospital, and "data does not leave the hospital". This strict internal data management policy is designed to maintain security and hospital-level control over clinical assets.Ethical Constraints: The Institutional Review Board (IRB) approved the study based on strict data protection measures. Sharing raw IPD externally might require additional informed consent from participants or further ethical amendments that were not part of the original approval (2026PHB097-001).Intellectual Property and Proprietary Algorithms: Since this study involves
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Comprehensive Evaluation of Musculoskeletal Aging and Degenerative Pathologies Using Multi-modal Clinical Imaging and Quantitative Analysis.
- ClinicalTrials.gov ID
- NCT07474571
- Acronym / sponsor ID
- 2026PHB097-001
- Conditions
- Osteoarthitis, Osteoporosis
- Keywords
- artificial intelligence, osteoarthitis, diagnosis, machine learning, osteoporosis, clinical images
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 1 Nov 2005 (actual)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 16 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
- Research Rationale and Goals Musculoskeletal aging often presents as a complex interplay between Osteoporosis (OP) and Osteoarthritis (OA). Despite their prevalence, current diagnostic workflows frequently treat these conditions in isolation, often relying on manual radiological staging that is prone to inter-observer variability. This study aims to develop and validate a multimodal artificial intelligence (AI) platform capable of simultaneous detection and precise disease staging for both OP and OA. By integrating diverse data sources-including clinical laboratory markers, patient history, and multiple imaging modalities (X-ray, CT, and MRI)-the project seeks to provide a holistic and objective assessment of skeletal health.
- Study Design and Population The research employs a bidirectional observational cohort study design. Retrospective Cohort (Model Development): Data will be collected from approximately 1,500 patients who visited the Peking University People's Hospital (PKUPH) between November 2005 and November 2025. Prospective Cohort (External Validation): At least 500 new participants will be recruited starting from December 2025 to test the model's performance in a real-world clinical setting. The study targets adults aged 18 to 90 who have completed relevant musculoskeletal imaging scans.
- Methodology and AI PipelineThe study is divided into three strategic phases:Phase I: Multimodal Data Integration: Collection of de-identified imaging (X-ray/CT/MR), laboratory indices (e.g., bone turnover markers, calcium-phosphorus metabolism), and clinical demographics (Age, BMI, medical history).Phase II: Intelligent Diagnostic Staging: Leveraging Convolutional Neural Networks (CNN) for image feature extraction and machine learning algorithms (e.g., XGBoost, SVM) for clinical feature fusion.For Osteoporosis: The model will categorize bone health stages (Normal, Osteopenia, Osteoporosis) using DXA T-scores as the gold standard.For Osteoarthritis: The system will automate grading based on the Kellgren-Lawrence (KL) scale, identifying joint space narrowing and osteophytic progression.Phase III: Validation and Explainability: Internal cross-validation and independent external testing using the prospective cohort. SHAP (Shapley Additive Explanations) analysis will be applied to quantify the contribution of each modality to the final diagnostic decision, ensuring clinical transparency.
- Outcome Measures The primary outcome is the diagnostic accuracy (AUC, Sensitivity, Specificity) of the AI model for both conditions. Secondary outcomes focus on long-term clinical utility, including the incidence of new fragility fractures and changes in functional scores (e.g., VAS or OKS) during the follow-up periods (6, 12, 24, and 36 months).
- Ethical Oversight and Data Safety The study is conducted at Peking University People's Hospital under the approval of the Institutional Review Board (Approval No. 2026PHB097-001). It adheres to GCP principles and the Declaration of Helsinki. All imaging and clinical data are strictly de-identified (anonymized) before being entered into the secure, encrypted research database to protect patient privacy.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| Peking University People's Hospital cohort 1 | – | Other: No Interventionsparticipants with osteoarthritis |
| Peking University People's Hospital cohort 2 | – | Other: No Interventionsparticipants with osteoporosis |
| Sun yat-sen memorial hospital 1 | – | Other: No Interventionsparticipants with osteoarthritis |
| Sun yat-sen memorial hospital 2 | – | Other: No Interventionsparticipants with osteoporosis |
| Peking University People's Hospital cohort 3 | – | Other: No Interventionsparticipants without osteoporosis and osteoarthritis |
| Sun yat-sen memorial hospital 3 | – | Other: No Interventionsparticipants without osteoporosis and osteoarthritis |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No InterventionsNo Interventions | Other | 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 |
Primary outcome
Bone age status
Time frame: From enrollment to the initial treatment at 2 years
Bone mineral density and osteoarthitis
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Mar 2026.
- Individual participant dataSharing planned: No. Privacy and Confidentiality Risks: The study involves sensitive clinical imaging (X-ray, CT, MRI) and laboratory data from patients at Peking University People's Hospital. Even after de-identification, there is a risk that high-resolution medical images could be re-identified, violating patient privacy agreements .Institutional Data Policy: The protocol states that data processing and export are restricted to computers registered within the hospital, and "data does not leave the hospital". This strict internal data management policy is designed to maintain security and hospital-level control over clinical assets.Ethical Constraints: The Institutional Review Board (IRB) approved the study based on strict data protection measures. Sharing raw IPD externally might require additional informed consent from participants or further ethical amendments that were not part of the original approval (2026PHB097-001).Intellectual Property and Proprietary Algorithms: Since this study involves
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Osteoarthitis and Osteoporosis, run by Peking University People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yuhui Kou, M.D (13146213332, yuhuikou@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Bone age status (time frame: From enrollment to the initial treatment at 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Mar 2026.