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Proceedings Paper

Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms
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Paper Abstract

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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140R (16 March 2020); doi: 10.1117/12.2551392
Show Author Affiliations
Nianyi Li, Duke Univ. (United States)
Albert Swiecicki, Duke Univ. (United States)
Nicholas Said, Duke Univ. (United States)
Jonathan O'Donnell, Duke Univ. (United States)
William A. Jiranek, Duke Univ. (United States)
Maciej A. Mazurowski, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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