Knee JointNCT07784686
Feature Analysis of Knee Joint Magnetic Resonance Imaging Based on Artificial Intelligence
An observational study of Knee Joint, run by Tang-Du Hospital at 1 site in China.
Trial summary
This retrospective observational study aims to investigate imaging features of the knee joint on magnetic resonance imaging (MRI) using artificial intelligence (AI)-based image analysis. Existing knee MRI examinations from eligible participants are retrospectively reviewed and analyzed. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Knee Joint
- How many people
- 9,500 participants
- Where
- 1 siteChina
- Study dates
- 2 Jul 2026to 1 Aug 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The study population consisted of patients who underwent knee MRI examinations at participating medical centers during the predefined study period. Eligible participants were retrospectively identified from existing imaging and clinical databases according to the predefined inclusion and exclusion criteria.
Who may be able to join
- Complete knee MRI data, including sagittal and coronal images.
- Adequate image quality without significant motion artifacts, metal artifacts, or magnetic susceptibility artifacts, with clear visualization of key anatomical structures of the knee.
- Complete demographic and clinical information available for study grouping and analysis.
Who cannot join
- Incomplete MRI data or poor image quality.
- History of knee surgery or implantation.
- Incomplete clinical information.
Trial locations and contacts
1 site in 1 country.
China1 site
Tangdu Hospital, Fourth Military Medical University
Xi'an, Shaanxi, 710038
About this study
This retrospective observational study aims to investigate imaging features of the knee joint on magnetic resonance imaging (MRI) using artificial intelligence (AI)-based image analysis. Existing knee MRI examinations from eligible participants are retrospectively reviewed and analyzed. AI methods are used to identify and characterize anatomical structures and imaging abnormalities of the knee and to quantitatively evaluate relevant imaging features. The study aims to assess the feasibility and performance of AI-assisted MRI analysis and to explore its potential value in improving the objective and reproducible evaluation of knee joint imaging.
Read the detailed description
This is a retrospective observational study based on previously acquired knee magnetic resonance imaging (MRI) data. Participants who underwent knee MRI examinations and met the predefined eligibility criteria are retrospectively included.
MRI images are analyzed using artificial intelligence-based image processing and analysis methods. The study focuses on the identification, segmentation, characterization, and quantitative assessment of knee joint structures and imaging abnormalities. Where applicable, AI-generated results are compared with reference assessments to evaluate model performance and the consistency of imaging measurements.
The study is intended to investigate the imaging characteristics of the knee joint, evaluate the performance and robustness of artificial intelligence-based MRI analysis methods, and explore their potential application in quantitative imaging assessment and computer-assisted evaluation of knee disorders.
- Conditions
- Knee Joint
- Keywords
- Knee Joint
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Retrospective Knee MRI Cohort | Participants who underwent knee MRI examinations as part of routine clinical care and met the predefined eligibility criteria were retrospectively included. Existing MRI images and relevant clinical information were used for artificial intelligence-based image analysis. No intervention was assigned to participants for the purpose of this study. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Dice Similarity Coefficient for AI-Based Knee MRI Segmentation
The Dice similarity coefficient will be used to evaluate the spatial agreement between artificial intelligence-generated segmentations and reference annotations. The Dice coefficient ranges from 0 to 1, with higher values indicating greater agreement.
Measured: At completion of retrospective MRI image analysis
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Aug 2026
- Study start2 Jul 2026
- Primary completion20 Jul 2026
- Study completion1 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Knee JointStudies of the same condition, open and under way.
- More trials run by Tang-Du 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
- Retrospective
- Enrolment
- 9,500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tang-Du Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07784686
- Sponsor's protocol ID
- K-HG-202607-01
- Status last verified
- Oct 2026
- First submitted
- 21 Aug 2026
- Last updated
- 13 Oct 2026
- Results posted
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Feature Analysis of Knee Joint Magnetic Resonance Imaging Based on Artificial Intelligence
- ClinicalTrials.gov ID
- NCT07784686
- Acronym / sponsor ID
- K-HG-202607-01
- Conditions
- Knee Joint
- Keywords
- Knee Joint
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9,500 participants (actual)
- Lead sponsor
- Tang-Du HospitalUniversity, hospital or other
- Study start
- 2 Jul 2026 (actual)
- Primary completion
- 20 Jul 2026 (actual)
- Study completion
- 1 Aug 2026 (actual)
- First posted
- 25 Aug 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.
This retrospective observational study aims to investigate imaging features of the knee joint on magnetic resonance imaging (MRI) using artificial intelligence (AI)-based image analysis. Existing knee MRI examinations from eligible participants are retrospectively reviewed and analyzed. AI methods are used to identify and characterize anatomical structures and imaging abnormalities of the knee and to quantitatively evaluate relevant imaging features. The study aims to assess the feasibility and performance of AI-assisted MRI analysis and to explore its potential value in improving the objective and reproducible evaluation of knee joint imaging.
Detailed description
This is a retrospective observational study based on previously acquired knee magnetic resonance imaging (MRI) data. Participants who underwent knee MRI examinations and met the predefined eligibility criteria are retrospectively included.
MRI images are analyzed using artificial intelligence-based image processing and analysis methods. The study focuses on the identification, segmentation, characterization, and quantitative assessment of knee joint structures and imaging abnormalities. Where applicable, AI-generated results are compared with reference assessments to evaluate model performance and the consistency of imaging measurements.
The study is intended to investigate the imaging characteristics of the knee joint, evaluate the performance and robustness of artificial intelligence-based MRI analysis methods, and explore their potential application in quantitative imaging assessment and computer-assisted evaluation of knee disorders.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retrospective Knee MRI Cohort | – | –Participants who underwent knee MRI examinations as part of routine clinical care and met the predefined eligibility criteria were retrospectively included. Existing MRI images and relevant clinical information were used for artificial intelligence-based image analysis. No intervention was assigned to participants for the purpose of this study. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consisted of patients who underwent knee MRI examinations at participating medical centers during the predefined study period. Eligible participants were retrospectively identified from existing imaging and clinical databases according to the predefined inclusion and exclusion criteria.
Key inclusion criteria (3 listed)
- Complete knee MRI data, including sagittal and coronal images.
- Adequate image quality without significant motion artifacts, metal artifacts, or magnetic susceptibility artifacts, with clear visualization of key anatomical structures of the knee.
- Complete demographic and clinical information available for study grouping and analysis.
Key exclusion criteria (3 listed)
- Incomplete MRI data or poor image quality.
- History of knee surgery or implantation.
- Incomplete clinical information.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Dice Similarity Coefficient for AI-Based Knee MRI Segmentation
Time frame: At completion of retrospective MRI image analysis
The Dice similarity coefficient will be used to evaluate the spatial agreement between artificial intelligence-generated segmentations and reference annotations. The Dice coefficient ranges from 0 to 1, with higher values indicating greater agreement.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Oct 2026.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Knee Joint, run by Tang-Du 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 3 inclusion and 3 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: finished, as of the sponsor's last update on 13 Oct 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Dice Similarity Coefficient for AI-Based Knee MRI Segmentation (time frame: At completion of retrospective MRI image analysis). 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 13 Oct 2026.