Degenerative Knee DisordersAI; R-CNNNCT07721116
Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction
An observational study of Degenerative Knee Disorders and Knee Osteoarthritis, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
This study aims to develop and validate an artificial intelligence (AI)-assisted platform for musculoskeletal knee ultrasonography and to establish an interpretable prediction model for clinical outcomes following ultrasound-guided injection therapies in patients with degenerative knee disorders. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Degenerative Knee Disorders, Knee Osteoarthritis, Musculoskeletal Ultrasonography
- What is being tested?
- artificial intelligence-assisted advanced analysis
- Type of treatment
- Injection or infusion
- How many people
- 310 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Jul 2026to 31 Dec 2029
- How long will I take part?
- 1 Day
- 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: The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability.
Who may be able to join
- Clinical diagnosis of healthy adult without major systemic disease
- Age ≥18 years
- Able to understand and follow study instructions
- Ambulatory without walking aids
- No pain in either knee for at least 6 months before enrollment
- Clinical diagnosis of radiographic knee osteoarthritis
Show all 22Show fewer
- Age ≥18 years
- Knee pain in at least one knee during the preceding year
- Medical records confirming knee pain, soreness, or stiffness within 1 month before enrollment
- Radiographic evidence of knee osteoarthritis, defined by at least one of the following:
- Kellgren-Lawrence grade ≥2 on anteroposterior radiographs
- Kellgren-Lawrence grade ≥2 on skyline (patellofemoral) radiographs
- Superior or inferior patellar osteophytes or posterior tibial osteophytes on lateral radiographs
- Clinical diagnosis of radiographic knee osteoarthritis requiring ultrasound-guided injection therapy
- Age ≥18 years
- Knee pain in at least one knee during the preceding year
- Medical records confirming knee pain, soreness, or stiffness within 1 month before enrollment
- Radiographic evidence of knee osteoarthritis, defined by at least one of the following:
- Kellgren-Lawrence grade ≥2 on anteroposterior radiographs
- Kellgren-Lawrence grade ≥2 on skyline (patellofemoral) radiographs
- Superior or inferior patellar osteophytes or posterior tibial osteophytes on lateral radiographs
- Willingness to undergo ultrasound-guided injection therapy and complete scheduled follow-up assessments
Who cannot join
- Previous knee surgery
- Rupture of one or more cruciate ligaments
- Knee injection within the preceding 6 months
- Major trauma involving the knee or periarticular region
- Rheumatic or autoimmune disease
- Objective 2: Development of an AI-Based Model for the Identification of Pathological Knee Structures
Show all 17Show fewer
- Systemic rheumatic disease (e.g., rheumatoid arthritis or ankylosing spondylitis)
- Malignancy
- Previous major knee trauma (including fracture)
- Previous knee surgery
- Intra-articular corticosteroid injection within the preceding 3 months
- Objective 3: Development of an AI-Assisted Predictive Model for Injection Treatment Outcomes
- Systemic rheumatic disease (e.g., rheumatoid arthritis or ankylosing spondylitis)
- Malignancy
- Previous major knee trauma (including fracture)
- Previous knee surgery
- Intra-articular corticosteroid injection within the preceding 3 months
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Hospital Beihu Branch
Taipei, Taiwan, 108206
Wei-Ting Wu, MD, +886223717101, wwtaustin@yahoo.com.tw
Ke-Vin Chang, MD, PhD, +886223717101, kvchang011@gmail.com
About this study
Official title: Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction for Ultrasound-Guided Injections
This study aims to develop and validate an artificial intelligence (AI)-assisted platform for musculoskeletal knee ultrasonography and to establish an interpretable prediction model for clinical outcomes following ultrasound-guided injection therapies in patients with degenerative knee disorders. The project seeks to improve the standardization, reproducibility, and clinical utility of knee ultrasound by reducing operator dependency and providing quantitative image analysis and outcome prediction.
The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures, including tendons, ligaments, cartilage, fat pads, and peripheral nerves. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability.
The anticipated outcome of this study is the development of a comprehensive AI-assisted knee ultrasound platform that supports standardized image interpretation, quantitative assessment of musculoskeletal pathology, and personalized prediction of treatment response to ultrasound-guided injection therapies in degenerative knee disorders.
Read the detailed description
High-resolution musculoskeletal ultrasonography has become a first-line imaging modality because it enables real-time visualization and dynamic assessment with high accessibility and low cost. Nevertheless, ultrasound remains highly operator-dependent, resulting in variability in image acquisition and interpretation, which limits standardization and widespread implementation, particularly for complex joints such as the knee. Building on our established expertise in computational ultrasound and deep-learning-assisted dynamic shoulder analysis, including patented artificial intelligence (AI)-derived quantitative biomarkers, this three-year project aims to develop an AI platform for advanced knee ultrasound analysis and to construct a predictive model for clinical outcomes following ultrasound-guided injections in degenerative knee disorders.
In the first year, we will establish a normative AI foundation model for knee ultrasonography by developing automated localization and multi-structure segmentation of major anatomical components, including tendons, ligaments, cartilage, fat pads, and peripheral nerves. Standardized acquisition protocols will be implemented to ensure consistent image quality. A Faster Region-Based Convolutional Neural Network (Faster R-CNN) framework incorporating ResNet50, a Feature Pyramid Network, and a Region Proposal Network will be used to detect key bony landmarks, followed by a multi-structure segmentation engine and quantitative feature extraction modules (e.g., thickness, surface regularity, and tissue heterogeneity). Segmentation performance will be evaluated using Intersection-over-Union and Dice coefficients, while measurement reliability will be assessed using intraclass correlation coefficients, standard error of measurement, minimal detectable change, and Bland-Altman analyses.
In the second year, the platform will be expanded to differentiate pathological patterns in knee tendons, ligaments, cartilage, and fat pads. Expert clinicians will label each segmented structure as normal or abnormal and further annotate clinically relevant subtypes, such as tendinopathy, calcification, partial or full-thickness tears, synovial hypertrophy or effusion, cartilage wear or exposure, and meniscal degeneration or tear. Supervised learning models will be trained for classification and evaluated using accuracy, precision, recall (sensitivity), and F1-score.
In the third year, we will develop an outcome prediction model for ultrasound-guided injections by integrating retrospective and prospective real-world data from approximately 150 patients receiving common injection therapies, including intra-articular hyaluronic acid, dextrose prolotherapy or platelet-rich plasma, and peripheral nerve-targeted interventions. Treatment success will be defined using validated patient-reported outcome measures, including the Knee Injury and Osteoarthritis Outcome Score and the Patient Acceptable Symptom State, incorporating minimal clinically important difference thresholds. Feature selection methods and cross-validation will be applied to mitigate overfitting. Model performance will be assessed using area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-score, and calibration metrics, with Shapley Additive exPlanations employed to enhance interpretability. External validation will be performed if additional datasets become available.
This project is expected to deliver the first systematic AI-based normative atlas for knee ultrasonography and an interpretable outcome prediction framework, improving diagnostic consistency, reducing operator dependency, and enabling personalized, evidence-informed injection strategies.
- Conditions
- Degenerative Knee Disorders, Knee Osteoarthritis, Musculoskeletal Ultrasonography, Artificial Intelligence
- Being tested
- artificial intelligence-assisted advanced analysis
- Keywords
- artificial intelligence, ultrasonography, knee, ultrasound-guided injections
- Medical subject headings
- Osteoarthritis, Knee
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence-assisted advanced analysisDiagnostic test
The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability.
What is being measured
The primary outcome is the main question the study is designed to answer.
AI Segmentation Performance for Normal Knee Structures
Performance of the artificial intelligence model in automatically identifying and segmenting normal knee anatomical structures on ultrasound images. Model performance will be evaluated using the Dice Similarity Coefficient (DSC) and Intersection-over-Union (IoU) by comparing AI-generated segmentation with expert manual annotations. Target structures include tendons, ligaments, cartilage, menisci, fat pads, and peripheral nerves.
Measured: Baseline (at ultrasound examination)
Diagnostic Accuracy of AI-Based Classification of Knee Pathologies
Diagnostic performance of the AI model in differentiating normal and pathological knee structures on ultrasound imaging. Performance will be evaluated using accuracy, sensitivity (recall), specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC), using expert ultrasound interpretation as the reference standard. Pathologies include tendinopathy, calcification, ligament sprain or tear, meniscal degeneration or tear, cartilage degeneration, synovitis, fat pad inflammation, and peripheral nerve enlargement.
Measured: Baseline (at ultrasound examination)
Accuracy of AI Prediction for Treatment Success
Performance of the AI-assisted prediction model in identifying patients who achieve successful clinical outcomes after ultrasound-guided injection therapy. Treatment success will be defined according to achievement of the Minimal Clinically Important Difference (MCID) in KOOS and/or attainment of the Patient Acceptable Symptom State (PASS). Predictive performance will be assessed using AUC, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
Measured: 3 months after ultrasound-guided injection
Secondary outcomes (5)
Knee Pain Intensity
Pain intensity assessed using the Visual Analog Scale (VAS; 0-10), with higher scores indicating greater pain severity.
Measured: Baseline, 1 month, and 3 months
Knee Function
Functional status assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), including pain, stiffness, and physical function subscales. Higher scores indicate worse symptoms and functional limitation.
Measured: Baseline, 1 month, and 3 months
Knee Injury and Osteoarthritis Outcome Score (KOOS)
Clinical improvement following ultrasound-guided injection therapy assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Higher scores indicate better knee function and fewer symptoms.
Measured: Baseline, 1 month, and 3 months
Patient Acceptable Symptom State (PASS)
Proportion of participants achieving a patient-acceptable symptom state following treatment according to validated PASS criteria.
Measured: 3 months after treatment
Reliability of Ultrasound Measurements
Intra-rater and inter-rater reliability of ultrasound measurements assessed using the Intraclass Correlation Coefficient (ICC), Standard Error of Measurement (SEM), Minimal Detectable Change (MDC), and Bland-Altman analysis.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jul 2026
- Study start1 Jul 2026 (estimated)
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Degenerative Knee DisordersStudies of the same condition, open and under way.
- More trials run by National Taiwan University 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
- Prospective
- Enrolment
- 310 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University Hospital Beihu Branch
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07721116
- Sponsor's protocol ID
- 202603036RINE
- Acronym
- AI; R-CNN
- Status last verified
- Jul 2026
- First submitted
- 5 Jul 2026
- Last updated
- 22 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction for Ultrasound-Guided Injections
- ClinicalTrials.gov ID
- NCT07721116
- Acronym / sponsor ID
- AI; R-CNN / 202603036RINE
- Conditions
- Degenerative Knee Disorders, Knee Osteoarthritis, Musculoskeletal Ultrasonography, Artificial Intelligence
- Keywords
- artificial intelligence, ultrasonography, knee, ultrasound-guided injections
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 310 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- National Taiwan University Hospital Beihu Branch
- Study start
- 1 Jul 2026 (estimated)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 22 Jul 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 study aims to develop and validate an artificial intelligence (AI)-assisted platform for musculoskeletal knee ultrasonography and to establish an interpretable prediction model for clinical outcomes following ultrasound-guided injection therapies in patients with degenerative knee disorders. The project seeks to improve the standardization, reproducibility, and clinical utility of knee ultrasound by reducing operator dependency and providing quantitative image analysis and outcome prediction.
The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures, including tendons, ligaments, cartilage, fat pads, and peripheral nerves. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability.
The anticipated outcome of this study is the development of a comprehensive AI-assisted knee ultrasound platform that supports standardized image interpretation, quantitative assessment of musculoskeletal pathology, and personalized prediction of treatment response to ultrasound-guided injection therapies in degenerative knee disorders.
Detailed description
High-resolution musculoskeletal ultrasonography has become a first-line imaging modality because it enables real-time visualization and dynamic assessment with high accessibility and low cost. Nevertheless, ultrasound remains highly operator-dependent, resulting in variability in image acquisition and interpretation, which limits standardization and widespread implementation, particularly for complex joints such as the knee. Building on our established expertise in computational ultrasound and deep-learning-assisted dynamic shoulder analysis, including patented artificial intelligence (AI)-derived quantitative biomarkers, this three-year project aims to develop an AI platform for advanced knee ultrasound analysis and to construct a predictive model for clinical outcomes following ultrasound-guided injections in degenerative knee disorders.
In the first year, we will establish a normative AI foundation model for knee ultrasonography by developing automated localization and multi-structure segmentation of major anatomical components, including tendons, ligaments, cartilage, fat pads, and peripheral nerves. Standardized acquisition protocols will be implemented to ensure consistent image quality. A Faster Region-Based Convolutional Neural Network (Faster R-CNN) framework incorporating ResNet50, a Feature Pyramid Network, and a Region Proposal Network will be used to detect key bony landmarks, followed by a multi-structure segmentation engine and quantitative feature extraction modules (e.g., thickness, surface regularity, and tissue heterogeneity). Segmentation performance will be evaluated using Intersection-over-Union and Dice coefficients, while measurement reliability will be assessed using intraclass correlation coefficients, standard error of measurement, minimal detectable change, and Bland-Altman analyses.
In the second year, the platform will be expanded to differentiate pathological patterns in knee tendons, ligaments, cartilage, and fat pads. Expert clinicians will label each segmented structure as normal or abnormal and further annotate clinically relevant subtypes, such as tendinopathy, calcification, partial or full-thickness tears, synovial hypertrophy or effusion, cartilage wear or exposure, and meniscal degeneration or tear. Supervised learning models will be trained for classification and evaluated using accuracy, precision, recall (sensitivity), and F1-score.
In the third year, we will develop an outcome prediction model for ultrasound-guided injections by integrating retrospective and prospective real-world data from approximately 150 patients receiving common injection therapies, including intra-articular hyaluronic acid, dextrose prolotherapy or platelet-rich plasma, and peripheral nerve-targeted interventions. Treatment success will be defined using validated patient-reported outcome measures, including the Knee Injury and Osteoarthritis Outcome Score and the Patient Acceptable Symptom State, incorporating minimal clinically important difference thresholds. Feature selection methods and cross-validation will be applied to mitigate overfitting. Model performance will be assessed using area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-score, and calibration metrics, with Shapley Additive exPlanations employed to enhance interpretability. External validation will be performed if additional datasets become available.
This project is expected to deliver the first systematic AI-based normative atlas for knee ultrasonography and an interpretable outcome prediction framework, improving diagnostic consistency, reducing operator dependency, and enabling personalized, evidence-informed injection strategies.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability.
Key inclusion criteria (22 listed)
- Clinical diagnosis of healthy adult without major systemic disease
- Age ≥18 years
- Able to understand and follow study instructions
- Ambulatory without walking aids
- No pain in either knee for at least 6 months before enrollment
Key exclusion criteria (17 listed)
- Previous knee surgery
- Rupture of one or more cruciate ligaments
- Knee injection within the preceding 6 months
- Major trauma involving the knee or periarticular region
- Rheumatic or autoimmune disease
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 |
|---|---|---|
| artificial intelligence-assisted advanced analysisThe study will be conducted in three phases. First, an AI foundation model for knee ultrasonography will be developed using standardized image acquisition protocols to enable automated localization, segmentation, and quantitative assessment of major anatomical structures. Second, supervised machine learning models will be trained to classify normal and pathological ultrasound findings, including common degenerative and inflammatory abnormalities affecting the knee. Third, retrospective and prospective clinical data from approximately 150 patients receiving ultrasound-guided injection therapies will be integrated to develop and validate a predictive model for treatment outcomes using imaging biomarkers and clinical variables. Treatment response will be evaluated using validated patient-reported outcome measures, and explainable AI methods will be applied to improve model interpretability. | Diagnostic test | – |
Primary outcome
AI Segmentation Performance for Normal Knee Structures
Time frame: Baseline (at ultrasound examination)
Performance of the artificial intelligence model in automatically identifying and segmenting normal knee anatomical structures on ultrasound images. Model performance will be evaluated using the Dice Similarity Coefficient (DSC) and Intersection-over-Union (IoU) by comparing AI-generated segmentation with expert manual annotations. Target structures include tendons, ligaments, cartilage, menisci, fat pads, and peripheral nerves.
Diagnostic Accuracy of AI-Based Classification of Knee Pathologies
Time frame: Baseline (at ultrasound examination)
Diagnostic performance of the AI model in differentiating normal and pathological knee structures on ultrasound imaging. Performance will be evaluated using accuracy, sensitivity (recall), specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC), using expert ultrasound interpretation as the reference standard. Pathologies include tendinopathy, calcification, ligament sprain or tear, meniscal degeneration or tear, cartilage degeneration, synovitis, fat pad inflammation, and peripheral nerve enlargement.
Accuracy of AI Prediction for Treatment Success
Time frame: 3 months after ultrasound-guided injection
Performance of the AI-assisted prediction model in identifying patients who achieve successful clinical outcomes after ultrasound-guided injection therapy. Treatment success will be defined according to achievement of the Minimal Clinically Important Difference (MCID) in KOOS and/or attainment of the Patient Acceptable Symptom State (PASS). Predictive performance will be assessed using AUC, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
5 secondary outcomes
Knee Pain Intensity
Time frame: Baseline, 1 month, and 3 months
Knee Function
Time frame: Baseline, 1 month, and 3 months
Knee Injury and Osteoarthritis Outcome Score (KOOS)
Time frame: Baseline, 1 month, and 3 months
Patient Acceptable Symptom State (PASS)
Time frame: 3 months after treatment
Reliability of Ultrasound Measurements
Time frame: Baseline
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 22 Jul 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.
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 Degenerative Knee Disorders and Knee Osteoarthritis, run by National Taiwan University Hospital at 1 site in Taiwan.
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 22 inclusion and 17 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 22 Jul 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 Taiwan. 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 Wei-Ting Wu, MD (+886223717101, wwtaustin@yahoo.com.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI Segmentation Performance for Normal Knee Structures (time frame: Baseline (at ultrasound examination)). The study lists 3 primary outcomes in all. 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 22 Jul 2026.