Deep learning of early brain imaging to predict post-arrest electroencephalography.

ABSTRACT:

Introduction

Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associations between CT and EEG might obviate the need to acquire both modalities. We quantified these associations via deep learning.

Methods

We performed a single-center, retrospective study including comatose patients hospitalized after cardiac arrest. We extracted brain CT DICOMs, resized and registered each to a standard anatomical atlas, performed skull stripping and windowed images to optimize contrast of the gray-white junction. We classified initial EEG as generalized suppression, other highly pathological findings or benign activity. We extracted clinical information available on presentation from our prospective registry. We

SUBMITTER: Elmer J 

PROVIDER: S-EPMC8923981 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature