Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging.

ABSTRACT:

Objective

We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.

Methods

151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionalit

SUBMITTER: Johnson GW 

PROVIDER: S-EPMC9149039 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature