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Abstract #4537

Learning how to see the invisible - using machine learning to find underlying abnormality patterns in reportedly normal MR brain images from patients with epilepsy

Oscar Bennett1, M. Jorge Cardoso1, John Duncan2,3, Gavin Winston2,3, and Sebastien Ourselin1

1Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom, 2Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom, 3Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom

The visual identification of subtle abnormalities in MR brain images that underlie focal epilepsies is a challenging problem. In this study, we used machine learning techniques to uncover patterns of abnormality that exist within reportedly normal brain images from individuals with epilepsy. Our results demonstrated that abnormalities exist in MR images reported to be normal by a human reader, and that these abnormalities exist in a different spatial pattern to that seen in visually apparent cases. We obtained novel insights into why visual assessment may be ineffective in these visually normal cases and provide suggestions on how to improve this situation.

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