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

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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.

Eligibility

Primary outcome measure(s)

• AI Segmentation Performance for Normal Knee Structures — 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 — 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 — 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.

Trial sites (1)

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