AI-assisted knee ultrasound analysis and outcome prediction

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study will develop and validate an AI-based system to automate knee ultrasound analysis, including localization, segmentation, and quantitative metrics. It will use standardized image acquisition to reduce operator variability and to create an interpretable model predicting clinical outcomes after ultrasound-guided injections. The project spans three years, starting with building a normative AI foundation for healthy knees, then training models to distinguish normal from pathological findings, and finally predicting treatment response in degenerative knee disorders. The approach builds on prior experience with computational ultrasound and aims to improve standardization and clinical utility of knee ultrasound imaging.

Why it matters

Who can join: the full rules

Objective 1: Development of an AI-Based Normative Model for the Healthy Knee

Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Inclusion Criteria:

* Clinical diagnosis of radiographic knee osteoarthritis
* 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

Exclusion Criteria:

* 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

Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Where it's running

• National Taiwan University Hospital Beihu Branch
  Taipei, Taiwan, Taiwan Recruiting

Study contacts

• Study coordinator: Wei-Ting Wu, MD
• Email: wwtaustin@yahoo.com.tw
• Phone: +886223717101

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.