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  1. Home
  3. Deep Learning-based Quantification Of Knee Effusion-synovitis Volume On Mri - Technique Development And Validation.
  1. Home
  3. Deep Learning-based Quantification Of Knee Effusion-synovitis Volume On Mri - Technique Development And Validation.

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Deep Learning-based Quantification of Knee Effusion-Synovitis Volume on MRI - Technique Development and Validation.

Adrian A Marth1, Felix Liu2, Ethan Pan2

  • 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Department of Radiology, Balgrist University Hospital, Zurich, Switzerland; Medical Faculty, University of Zurich, Zurich, Switzerland.

Osteoarthritis and Cartilage
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A deep learning model accurately quantifies knee effusion-synovitis volume (ESV) on MRI. This automated ESV measurement shows stronger associations with osteoarthritis features and symptoms than traditional scoring methods.

Keywords:
BiomarkersDeep LearningKneeMagnetic Resonance ImagingOsteoarthritisSynovitis

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Area of Science:

  • Biomedical Imaging
  • Artificial Intelligence in Medicine
  • Osteoarthritis Research

Background:

  • Knee osteoarthritis (OA) is a significant cause of disability.
  • Accurate quantification of knee effusion-synovitis volume (ESV) is crucial for OA research.
  • Current semiquantitative scoring methods may lack precision.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automatic quantification of knee effusion-synovitis volume (ESV) on MRI.
  • To assess the correlation between DL-derived ESV and semiquantitative effusion-synovitis (sqES) scores.
  • To compare the associations of ESV and sqES with MRI features and knee OA symptoms.

Main Methods:

  • A DL model was trained and tested on knee MRIs from the Osteoarthritis Initiative.
  • Segmentation performance was evaluated using Dice coefficients.
  • Spearman correlations and linear models were used to compare ESV and sqES with various scoring systems (WORMS, MOAKS) and clinical outcomes (WOMAC).

    • Main Results:

    • The DL model achieved a mean Dice coefficient of 0.79, indicating good segmentation performance.
    • ESV showed moderate to strong correlations with established semiquantitative scores (WORMS: ρ=0.50, MOAKS: ρ=0.65).
    • DL-derived ESV demonstrated stronger associations with MRI features and knee OA symptoms compared to sqES scores.

    Conclusions:

    • A validated DL model enables automated quantification of knee ESV from MRI.
    • Automated ESV quantification shows promise as a scalable imaging biomarker for OA research.
    • Further validation in independent cohorts is recommended to confirm the clinical utility of this DL model.