Classification of cardioembolic stroke based on a deep neural network using chest radiographs.

ABSTRACT:

Background

Although chest radiographs have not been utilised well for classifying stroke subtypes, they could provide a plethora of information on cardioembolic stroke. This study aimed to develop a deep convolutional neural network that could diagnose cardioembolic stroke based on chest radiographs.

Methods

Overall, 4,064 chest radiographs of consecutive patients with acute ischaemic stroke were collected from a prospectively maintained stroke registry. Chest radiographs were randomly partitioned into training/validation (n = 3,255) and internal test (n = 809) datasets in an 8:2 ratio. A densely connected convolutional network (ASTRO-X) was trained to diagnose cardioembolic stroke based on chest radiographs. The performance of ASTRO-X was evaluated using the area under the

SUBMITTER: Jeong HG 

PROVIDER: S-EPMC8264106 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature