Dataset Information

Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.

ABSTRACT:

Background and purpose

This study aimed to investigate the applicability of deep learning (DL) model using diffusion-weighted imaging (DWI) data to predict the severity of aphasia at an early stage in acute stroke patients.

Methods

We retrospectively analyzed consecutive patients with aphasia caused by acute ischemic stroke in the left middle cerebral artery territory, who visited Asan Medical Center between 2011 and 2013. To implement the DL model to predict the severity of post-stroke aphasia, we designed a deep feed-forward network and utilized the lesion occupying ratio from DWI data and established clinical variables to estimate the aphasia quotient (AQ) score (range, 0 to 100) of the Korean version of the Western Aphasia Battery. To evaluate the performance of the DL model, we analyzed Cohen's weighted kappa with linear weights for the categorized AQ score (0-25, very severe; 26-50, severe; 51-75, moderate; ≥76, mild) and Pearson's correlation coefficient for continuous values.

Results

We identified 225 post-stroke aphasia patients, of whom 176 were included and analyzed. For the categorized AQ score, Cohen's weighted kappa coefficient was 0.59 (95% confidence interval [CI], 0.42 to 0.76; P<0.001). For continuous AQ score, the correlation coefficient between true AQ scores and model-estimated values was 0.72 (95% CI, 0.55 to 0.83; P<0.001).

Conclusions

DL approaches using DWI data may be feasible and useful for estimating the severity of aphasia in the early stage of stroke.

SUBMITTER: Jeong S

PROVIDER: S-EPMC8829479 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.

Jeong Soo S Lee Eun-Jae EJ Kim Yong-Hwan YH Woo Jin Cheol JC Ryu On-Wha OW Kwon Miseon M Kwon Sun U SU Kim Jong S JS Kang Dong-Wha DW

Journal of stroke 20220131 1

<h4>Background and purpose</h4>This study aimed to investigate the applicability of deep learning (DL) model using diffusion-weighted imaging (DWI) data to predict the severity of aphasia at an early stage in acute stroke patients.<h4>Methods</h4>We retrospectively analyzed consecutive patients with aphasia caused by acute ischemic stroke in the left middle cerebral artery territory, who visited Asan Medical Center between 2011 and 2013. To implement the DL model to predict the severity of post- ...[more]

PMID: 35135064

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Dataset Information

Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.

Background and purpose

Methods

Results

Conclusions

Publications

Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.

Similar Datasets

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