OMERACT validation of a deep learning algorithm for automated absolute quantification of knee joint effusion versus manual semi-quantitative assessment

Semin Arthritis Rheum. 2024 Jun:66:152420. doi: 10.1016/j.semarthrit.2024.152420. Epub 2024 Feb 17.

Abstract

Objective: To begin evaluating deep learning (DL)-automated quantification of knee joint effusion-synovitis via the OMERACT filter.

Methods: A DL algorithm previously trained on Osteoarthritis Initiative (OAI) knee MRI automatically quantified effusion volume in MRI of 53 OAI subjects, which were also scored semi-quantitatively via KIMRISS and MOAKS by 2-6 readers.

Results: DL-measured knee effusion correlated significantly with experts' assessments (Kendall's tau 0.34-0.43) CONCLUSION: The close correlation of automated DL knee joint effusion quantification to KIMRISS manual semi-quantitative scoring demonstrated its criterion validity. Further assessments of discrimination and truth vs. clinical outcomes are still needed to fully satisfy OMERACT filter requirements.

Keywords: Deep learning; Effusion-synovitis; MRI; OMERACT; Osteoarthritis.

Publication types

  • Validation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Algorithms
  • Deep Learning*
  • Female
  • Humans
  • Knee Joint* / diagnostic imaging
  • Knee Joint* / pathology
  • Magnetic Resonance Imaging* / methods
  • Male
  • Middle Aged
  • Osteoarthritis, Knee* / diagnostic imaging
  • Reproducibility of Results