Classification of cardioembolic stroke based on a deep neural network using chest radiographsopen access
- Authors
- Jeong, Han-Gil; Kim, Beom Joon; Kim, Tackeun; Kang, Jihoon; Kim, Jun Yup; Kim, Joonghee; Kim, Joon-Tae; Park, Jong-Moo; Kim, Jae Guk; Hong, Jeong-Ho; Lee, Kyung Bok; Park, Tai Hwan; Kim, Dae-Hyun; Oh, Chang Wan; Han, Moon-Ku; Bae, Hee-Joon
- Issue Date
- Jul-2021
- Publisher
- Elsevier BV
- Keywords
- Stroke; Chest radiograph; Deep learning; Cardioembolism; Classification
- Citation
- EBioMedicine, v.69
- Journal Title
- EBioMedicine
- Volume
- 69
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/18718
- DOI
- 10.1016/j.ebiom.2021.103466
- ISSN
- 2352-3964
- 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 receiver operating characteristic curve. Gradientweighted class activation mapping was used to evaluate the region of focus of ASTRO-X. External testing was performed with 750 chest radiographs of patients with acute ischaemic stroke from 7 hospitals. Findings: The areas under the receiver operating characteristic curve of ASTRO-X were 0.86 (95% confidence interval [CI], 0.83-0.89) and 0.82 (95% CI, 0.79-0.85) during the internal and multicentre external testing, respectively. The gradient-weighted class activation map demonstrated that ASTRO-X was focused on the area where the left atrium was located. Compared with cases predicted as non-cardioembolism by ASTRO-X, cases predicted as cardioembolism by ASTRO-X had higher left atrial volume index and lower left ventricular ejection fraction in echocardiography. Interpretation: ASTRO-X, a deep neural network developed to diagnose cardioembolic stroke based on chest radiographs, demonstrated good classification performance and biological plausibility. (C) 2021 The Author(s). Published by Elsevier B.V.
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Collections - College of Medicine > Department of Neurology > 1. Journal Articles
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