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Osteoarthritis (OA) classification in the knee is most commonly done with radiographs using the 0-4 Kellgren Lawrence (KL) grading system where 0 is normal, 1 shows doubtful signs of OA, 2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading is widely used for clinical assessment and diagnosis of OA, usually on a high volume of radiographs, making its automation highly relevant. We propose a fully automated algorithm for the detection of OA using KL gradings with a state-of-the-art neural network. Four thousand four hundred ninety bilateral PA fixed-flexion knee radiographs were collected from the Osteoarthritis Initiative dataset (age = 61.2 ± 9.2 years, BMI = 32.8 ± 15.9 kg/m 2 , 42/58 male/female split) for six different time points. The left and right knee joints were localized using a U-net model. These localized images were used to train an ensemble of DenseNet neural network architectures for the prediction of OA severity. This ensemble of DenseNets' testing sensitivity rates of no OA, mild, moderate, and severe OA were 83.7, 70.2, 68.9, and 86.0% respectively. The corresponding specificity rates were 86.1, 83.8, 97.1, and 99.1%. Using saliency maps, we confirmed that the neural networks producing these results were in fact selecting the correct osteoarthritic features used in detection. These results suggest the use of our automatic classifier to assist radiologists in making more accurate and precise diagnosis with the increasing volume of radiographic image being taken in clinic.

Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs