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Abstract #1242

Automated Textural Classification of Osteoarthritis Magnetic Resonance Images

Joshua D Kaggie1,2, Rob Tovey3, James MacKay1,2, Fiona J Gilbert1,2, Ferdia Gallagher1,2, Andrew McCaskie2,4, and Martin J Graves1,2

1Radiology, University of Cambridge, Cambridge, United Kingdom, 2Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, 3Mathematics, University of Cambridge, Cambridge, United Kingdom, 4Division of Trauma and Orthopaedic Surgery, University of Cambridge, Cambridge, United Kingdom

Osteoarthritis (OA) is the most common cause of disability in the United Kingdom and United States. Identifying the rate of OA progression remains an important clinical and research challenge for early disease monitoring. Texture analysis of tibial subchondral bone using magnetic resonance imaging (MRI) has demonstrated the ability to discriminate between different stages of OA. This work combines texture analysis with machine learning methods (Lasso, Decision Tree, and Neural Network) to predict radiographic disease progression over 3 years, trained using data from the Osteoarthritis Initiative. We achieved high sensitivity (86%), specificity (64%) and accuracy (74%) for predictions of OA progression.

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