Due to a lack of objective markers, trigeminal neuralgia is difficult to diagnose and classify. This difficulty results in frequent pain recurrences following medication and invasive therapies. In this work, we show that a radiomics based model with features extracted from MRI images can be used to identify painful nerves. Using a cohort of trigeminal neuralgia patients, our predictive model achieves an accuracy of 78% and an AUC of 0.84 in distinguishing between nerves affected and nonaffected by trigeminal neuralgia.
This abstract and the presentation materials are available to members only; a login is required.