Related Experiment Video
Updated: Jul 2, 2025
Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
Published on: March 18, 2022
OMERACT validation of a deep learning algorithm for automated absolute quantification of knee joint effusion versus
Banafshe Felfeliyan1, Stephanie Wichuk1, Abhilash R Hareendranathan1
1Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada.
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.
