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Abstract #5660

Predicting Osteoarthritis Radiographic Incidence by Coupling Quantitative Compositional MRI and Deep Learning

Valentina Pedoia1, Jan Neumann 1, Ursula Heilmeier 1, Jenny Haefeli1, Adam R Ferguson1, Thomas Link1, and Sharmila Majumdar1

1University of California, San Francisco, San Francisco, CA, United States

In this study quantitative compositional MRI and deep learning were coupled to discover latent feature representations, non-linear aggregation among elementary features able to characterize relaxation maps for Osteoarthritis diagnosis and progression prediction. 1,348 subjects from the Osteoarthritis Initiative (OAI) public dataset were considered. T2 relaxation map were automatically analyzed to build a 2D feature map used to train a convolutional neural network for the classification of subjects in OA, control and progression groups. The proposed method was able to detect OA subjects with 95.2% accuracy, and to detect controls subjects that demonstrated OA signs 4 years later with 80.7% accuracy.

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