Abstract #1895

GAMER-MRI and modified Layer-wise Relevance Propagation identify on quantitative MRI regions sensitive to clinical disability in MS patients

Po-Jui Lu^1,2,3, Benjamin Odry⁴, Muhamed Barakovic^1,2,3, Matthias Weigel^1,2,3,5, Robin Sandkühler⁶, Reza Rahmanzadeh⁷, Xinjie Chen^1,2,3, Mario Ocampo-Pineda^1,2,3, Jens Kuhle^2,3, Ludwig Kappos^2,3, Philippe Cattin⁶, and Cristina Granziera^1,2,3

¹Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ²Department of Neurology, University Hospital Basel, Basel, Switzerland, ³Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland, ⁴AI for Clinical Analytics, Covera Health, New York, NY, United States, ⁵Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, ⁶Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland, ⁷Neuroradiology Department, Inselspital, Bern, Switzerland

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Multiple SclerosisThe decision process of artificial intelligence is elusive. We proposed a new method that by combining an attention-based convolutional neural network (GAMER-MRI) with the modified Layer-wise Relevance Propagation could reveal relevant regions on quantitative imaging maps in differentiating multiple sclerosis patients with mild-moderate and severe disabilities. The assessment of the relevant regions included the impact of inverting values within the regions and the heatmap on the MNI152 template. Our results show good network performance and identify brain regions relevant to the corticospinal tract. The proposed method might be useful to further explore patterns of brain microstructural alterations associated with disability.

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