Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction

NCT07721116 | Recruiting

• 1 other identifier: 202603036RINE
• Study Type: observational
• Target: 310
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Degenerative Knee Disorders; Knee Osteoarthritis; Musculoskeletal Ultrasonography; Artificial Intelligence

Keywords

artificial intelligence; ultrasonography; knee; ultrasound-guided injections

Outcome Measures

Primary Outcomes (3)

• 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.

  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.

  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.

  3 months after ultrasound-guided injection

Secondary Outcomes (5)

• Knee Pain Intensity

  Baseline, 1 month, and 3 months

• Knee Function

  Baseline, 1 month, and 3 months

• Knee Injury and Osteoarthritis Outcome Score (KOOS)

  Baseline, 1 month, and 3 months

• Patient Acceptable Symptom State (PASS)

  3 months after treatment

• Reliability of Ultrasound Measurements

  Baseline

Interventions

artificial intelligence-assisted advanced analysis DIAGNOSTIC_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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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

You may not qualify if:

• 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
• 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
• Systemic rheumatic disease (e.g., rheumatoid arthritis or ankylosing spondylitis)

Sponsors & Collaborators

• National Taiwan University Hospital: lead
• National Taiwan University Hospital Beihu Branch: collaborator

Study Sites (1)

National Taiwan University Hospital Beihu Branch

Taipei, Taiwan, 108206, Taiwan

RECRUITING

MeSH Terms

Conditions

Osteoarthritis, Knee

Condition Hierarchy (Ancestors)

Osteoarthritis; Arthritis; Joint Diseases; Musculoskeletal Diseases; Rheumatic Diseases

Central Study Contacts

Wei-Ting Wu, MD

CONTACT

+886223717101; wwtaustin@yahoo.com.tw

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 5, 2026
• First Posted: July 22, 2026
• Study Start: July 1, 2026
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029
• Last Updated: July 22, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)