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Abstract #3086

Deep learning for prognosis in Degenerative cervical myelopathy

Lucas Rouhier1, Matthieu Parizet1, Muhammad Ali Akbar2, Michael Weber3, Michael G. Fehlings2, and Julien Cohen-Adad1,4

1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, 2Division of Neurosurgery, Departement of surgery, University of Toronto, Toronto, ON, Canada, 3Departement of surgery, McGill University, Montreal, QC, Canada, 4Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada

Degenerative cervical myelopathy is an important cause of spinal cord dysfunction in adults worldwide 1,2. This study’s goal is to use boosting algorithm and deep learning on MRI and clinical data to predict the condition of a patient 6 months after baseline. Results show an improvement of prediction accuracy when combining MRI with clinical data (82.3%) versus with clinical data only (78.5%). The heterogeneity of the data makes it difficult for the learning algorithm to generalize, however future work exploiting boosting algorithm for structural data, and dimensionality reduction (e.g., via MRI feature extraction) could further improve prognosis accuracy.

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