  Knee osteoarthritis (OA) is the most common musculoskeletal disease in the
world. In primary healthcare, knee OA is diagnosed using clinical examination
and radiographic assessment. Osteoarthritis Research Society International
(OARSI) atlas of OA radiographic features allows to perform independent
assessment of knee osteophytes, joint space narrowing and other knee features.
This provides a fine-grained OA severity assessment of the knee, compared to
the gold standard and most commonly used Kellgren-Lawrence (KL) composite
score. However, both OARSI and KL grading systems suffer from moderate
inter-rater agreement, and therefore, the use of computer-aided methods could
help to improve the reliability of the process. In this study, we developed a
robust, automatic method to simultaneously predict KL and OARSI grades in knee
radiographs. Our method is based on Deep Learning and leverages an ensemble of
deep residual networks with 50 layers, squeeze-excitation and ResNeXt blocks.
Here, we used transfer learning from ImageNet with a fine-tuning on the whole
Osteoarthritis Initiative (OAI) dataset. An independent testing of our model
was performed on the whole Multicenter Osteoarthritis Study (MOST) dataset. Our
multi-task method yielded Cohen's kappa coefficients of 0.82 for KL-grade and
0.79, 0.84, 0.94, 0.83, 0.84, 0.90 for femoral osteophytes, tibial osteophytes
and joint space narrowing for lateral and medial compartments respectively.
Furthermore, our method yielded area under the ROC curve of 0.98 and average
precision of 0.98 for detecting the presence of radiographic OA (KL $\geq 2$),
which is better than the current state-of-the-art.
