MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls.

ABSTRACT:

Background

Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies.

Method

We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types.

Results

We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.8

SUBMITTER: Chang AJ 

PROVIDER: S-EPMC9970972 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature