Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.

ABSTRACT:

Background and purpose

This study aimed to investigate the applicability of deep learning (DL) model using diffusion-weighted imaging (DWI) data to predict the severity of aphasia at an early stage in acute stroke patients.

Methods

We retrospectively analyzed consecutive patients with aphasia caused by acute ischemic stroke in the left middle cerebral artery territory, who visited Asan Medical Center between 2011 and 2013. To implement the DL model to predict the severity of post-stroke aphasia, we designed a deep feed-forward network and utilized the lesion occupying ratio from DWI data and established clinical variables to estimate the aphasia quotient (AQ) score (range, 0 to 100) of the Korean version of the Western Aphasia Battery. To evaluate the performance of the DL m

SUBMITTER: Jeong S 

PROVIDER: S-EPMC8829479 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature