  The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a
central criteria for the use of total knee arthroplasty. However, this
assessment suffers from imprecise standards and a remarkably high inter-reader
variability. An algorithmic, automated assessment of KOA severity could improve
overall outcomes of knee replacement procedures by increasing the
appropriateness of its use. We propose a novel deep learning-based five-step
algorithm to automatically grade KOA from posterior-anterior (PA) views of
radiographs: (1) image preprocessing (2) localization of knees joints in the
image using the YOLO v3-Tiny model, (3) initial assessment of the severity of
osteoarthritis using a convolutional neural network-based classifier, (4)
segmentation of the joints and calculation of the joint space narrowing (JSN),
and (5), a combination of the JSN and the initial assessment to determine a
final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation
masks used to make the assessment, our algorithm demonstrates a higher degree
of transparency compared to typical "black box" deep learning classifiers. We
perform a comprehensive evaluation using two public datasets and one dataset
from our institution, and show that our algorithm reaches state-of-the art
performance. Moreover, we also collected ratings from multiple radiologists at
our institution and showed that our algorithm performs at the radiologist
level.
  The software has been made publicly available at
https://github.com/MaciejMazurowski/osteoarthritis-classification.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning