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NCT07721116

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

Sponsor: National Taiwan University Hospital

View on ClinicalTrials.gov

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.

Official title: Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction for Ultrasound-Guided Injections

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

OBSERVATIONAL

Enrollment

310

Start Date

2026-07-01

Completion Date

2029-12-31

Last Updated

2026-07-22

Healthy Volunteers

Yes

Conditions

Degenerative Knee Disorders Knee Osteoarthritis Musculoskeletal Ultrasonography Artificial Intelligence

Interventions

DIAGNOSTIC_TEST

artificial intelligence-assisted advanced analysis

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.

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

Locations (1)

National Taiwan University Hospital Beihu Branch

Taipei, Taiwan, Taiwan