Keywords: Epilepsy, Perfusion, ASL, CBF . mTLE, Machin learning
Motivation: Utilizing perfusion features to differentiate left and right mesial temporal lobe epilepsy (mTLE) using machine learning.
Goal(s): The study aims to assess ASL MRI's perfusion analysis ability to identify abnormalities in brain regions for distinguishing between mTLE cases and normal cohorts.
Approach: Cerebral blood flow obtained features used by different machine learning classifiers to separate right and left mTLE form control cohort.
Results: The utilization of CBF features proved valuable and effective in the machine learning-based classification of right and left mTLE data from the control cohort.
Impact: This study's outcomes benefit medical professionals and drug-resistant mTLE patients by expediting surgical assessments and enhancing treatment outcomes through improved lateralization and epilepsy classification.
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