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Related Concept Videos

Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
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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; Faculty of Medicine, University of Zurich, Zurich, Switzerland.

Osteoarthritis and Cartilage
|June 22, 2026
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.