Knee osteoarthritis (OA) is a prevalent and disabling degenerative joint disease. Objectively identifying knee OA severity is challenging given significant inter-reader variability due to human interpretation factors. The Kellgren-Lawrence (KL) grading system is a commonly used scale to quantitatively characterize the severity of knee OA in knee radiographs. It is important to reliably identify severe knee OA since total knee arthroplasty (TKA) can provide significant improvement in patient quality of life for patients with severe knee OA. In this study, we demonstrate a deep learning approach to automatically assessing KL grades. Our approach uses faster R-CNN object detection network to identify the knee region and deep convolutional neural network for classification. We used a dataset of 7962 knee radiographs for each posteroanterior (PA) and lateral (LAT) views, to develop and evaluate our approach. Images with their corresponding KL grades were obtained from the Multicenter Osteoarthritis Study (MOST) dataset. Our network showed multi-class classification accuracy of 69.15 % when the assessment was made based on PA views and accuracy of 56.68 % when LAT views were used. The developed network may play a significant role in surgical decision-making regarding knee replacement surgery.
Evaluating the severity of knee osteoarthritis (OA) accounts for significant plain film workload and is a crucial component of knee radiograph interpretation, which informs surgical decision-making for costly and invasive procedures such as knee replacement. The Kellgren-Lawrence (KL) grading scale systematically and quantitatively assesses the severity of knee OA but is associated with notable inter-reader variability. In this study, we propose a deep learning method for the assessment of joint space narrowing (JSN) in the knee, which is an essential part of determining the KL grade. To determine the extent of JSN, we analyzed 99 knee radiographs to calculate the distance between the femur and tibia. Our algorithm's measurements of JSN and KL grade correlated well other radiologists' assessments. The average distance (in pixels) between the femur and tibia bones as measured by our algorithm was 9.60 for KL=0, 7.60 for KL=1, 6.89 for KL=2, 3.75 for KL=3, 1.25 for KL=4. Additionally, we used 100 manually annotated knee radiographs to train the algorithm to segment the femur and tibia bones. When evaluated on an independent set of 20 knee radiographs, the algorithm demonstrated a Dice coefficient of 96.59%. An algorithm for measurement of JSN and KL grades may play a significant role in automatically, reliably, and passively evaluating knee OA severity and influence and surgical decision-making and treatment pathways.
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