Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images.

ABSTRACT: Purpose: To develop a preliminary deep learning model that uses diffusion-weighted imaging (DWI) images to classify the severity of neurological impairment caused by ischemic stroke. Materials and Methods: This retrospective study included 851 ischemic stroke patients (711 patients in the training set and 140 patients in the test set). The patients' NIHSS scores, which reflect the severity of neurological impairment, were reviewed upon admission and on Day 7 of hospitalization and were classified into two stages (stage 1 for NIHSS < 5 and stage 2 for NIHSS ≥ 5). A 3D-CNN was trained to predict the stage of NIHSS based on different preprocessed DWI images. The performance in predicting the severity of anterior and posterior circulation stroke was also investigated. The AUC,

SUBMITTER: Zeng Y 

PROVIDER: S-EPMC9325315 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature