ArticleJournal of stroke2022
Deep Learning Approach Using Diffusion-Weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients.
Article in Journal of stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging.Scientific reports · 2024Article
- Digital symbol-digit modalities test with modified flexible protocols in patients with CNS demyelinating diseases.Scientific reports · 2024Article
- AI-assisted assessment and treatment of aphasia: a review.Frontiers in public health · 2024Review
- Machine learning models in post-stroke aphasia: a scoping review.Frontiers in neurologyReview
- Article
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Authors and funding
9 authors.
Funding
Abstract
background and purposeThis 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.
methodsWe 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.
resultsWe 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).
conclusionsDL approaches using DWI data may be feasible and useful for estimating the severity of aphasia in the early stage of stroke.
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