  The severity of knee osteoarthritis is graded using the 5-point
Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the
subsequent grades 1-4 represent increasing severity of the affliction. Although
several methods have been proposed in recent years to develop models that can
automatically predict the KL grade from a given radiograph, most models have
been developed and evaluated on datasets not sourced from India. These models
fail to perform well on the radiographs of Indian patients. In this paper, we
propose a novel method using convolutional neural networks to automatically
grade knee radiographs on the KL scale. Our method works in two connected
stages: in the first stage, an object detection model segments individual knees
from the rest of the image; in the second stage, a regression model
automatically grades each knee separately on the KL scale. We train our model
using the publicly available Osteoarthritis Initiative (OAI) dataset and
demonstrate that fine-tuning the model before evaluating it on a dataset from a
private hospital significantly improves the mean absolute error from 1.09 (95%
CI: 1.03-1.15) to 0.28 (95% CI: 0.25-0.32). Additionally, we compare
classification and regression models built for the same task and demonstrate
that regression outperforms classification.
