Dataset Information

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 receiver operating characteristic curve. Gradient-weighted 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.

Funding

Grant No. 14-2020-046 and 08-2016-051 from the Seoul National University Bundang Research Fund and NRF-2020M3E5D9079768 from the National Research Foundation of Korea.

SUBMITTER: Jeong HG

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jeong Han-Gil HG Kim Beom Joon BJ Kim Tackeun T Kang Jihoon J Kim Jun Yup JY Kim Joonghee J Kim Joon-Tae JT Park Jong-Moo JM Kim Jae Guk JG Hong Jeong-Ho JH Lee Kyung Bok KB Park Tai Hwan TH Kim Dae-Hyun DH Oh Chang Wan CW Han Moon-Ku MK Bae Hee-Joon HJ

EBioMedicine 20210703

<h4>Background</h4>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.<h4>Methods</h4>Overall, 4,064 chest radiographs of consecutive patients with acute ischaemic stroke were collected from a prospectively maintained stroke registry. Chest radiographs were ran ...[more]

PMID: 34229276

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Dataset Information

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

Background

Methods

Findings

Interpretation

Funding

Publications

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

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