Artificial Intelligence-Assisted Advanced Analysis of Knee Imaging and Outcome Prediction (AI; R-CNN)

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.

Study Overview

• Status: Recruiting
• Conditions: Knee Osteoarthritis; Artificial Intelligence; Musculoskeletal Ultrasonography; Degenerative Knee Disorders
• Intervention / Treatment: Diagnostic test: artificial intelligence-assisted advanced analysis

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 Type: Observational
• Enrollment (Estimated): 310

Contacts and Locations

• Study Contact: Name: Wei-Ting Wu, MD; Phone Number: +886223717101; Email: wwtaustin@yahoo.com.tw

Study Locations

• Taiwan
  • Taiwan
    • Taipei, Taiwan, Taiwan, 108206
      • Recruiting
      • National Taiwan University Hospital Beihu Branch
      • Contact: Wei-Ting Wu, MD; Phone Number: +886223717101; Email: wwtaustin@yahoo.com.tw
      • Contact: Ke-Vin Chang, MD, PhD; Phone Number: +886223717101; Email: kvchang011@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Knee Pain Intensity	Pain intensity assessed using the Visual Analog Scale (VAS; 0-10), with higher scores indicating greater pain severity.	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.	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.	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.	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.	Baseline

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Collaborators: National Taiwan University Hospital Beihu Branch

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029

Study Registration Dates

• First Submitted: July 5, 2026
• First Submitted That Met QC Criteria: July 19, 2026
• First Posted (Actual): July 22, 2026

Study Record Updates

• Last Update Posted (Actual): July 22, 2026
• Last Update Submitted That Met QC Criteria: July 19, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: knee; artificial intelligence; ultrasonography; ultrasound-guided injections
• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Arthritis; Joint Diseases; Rheumatic Diseases; Osteoarthritis; Osteoarthritis, Knee
• Other Study ID Numbers: 202603036RINE

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No