J Stroke.  2022 Jan;24(1):108-117. 10.5853/jos.2021.02061.

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

• Jeong S
• Lee EJ
• Kim YH
• Woo JC
• Ryu OW
• Kwon M
• Kwon SU
• Kim JS
• Kang DW

• KMID: 2525336
• DOI: http://doi.org/10.5853/jos.2021.02061

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.

Keyword

Stroke; Aphasia; Magnetic resonance imaging; Deep learning

Figure

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  Figure 1. Flowchart showing patient selection. DWI, diffusion-weighted image; MCA, middle cerebral artery; K-WAB, Korean version of the Western Aphasia Battery.

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  Figure 2. Lesion pattern heat maps of (A) training and (B) test groups. A heat map was used to visualize the proportion of lesions in each voxel. We compared the lesion proportion in every voxel between the training and test groups using the Bernoulli model-based two-sample t-test, but found no difference between the training and test groups (P>0.05).

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  Figure 3. Correlation analysis between the true aphasia quotient (AQ) score and predicted AQ score in the test set. The correlation coefficient was 0.72 (95% confidence interval, 0.55 to 0.83; P<0.001); solid black line, regression line; dotted black line, 95% confidence limit; dotted circle, cases with notable discrepancy between the true AQ score and predicted AQ score with studentized residual larger than 2 (in absolute value).

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  Figure 4. Imaging characteristics of cases with notable discrepancies. Panels represent (A-D) Case 1, (E-H) Case 2, and (I-L) Case 3, respectively. (A, B) Small acute lesions in the left internal carotid artery (ICA) border zone (yellow arrowheads) on diffusion-weighted imaging (DWI); (C) increased time-to-peak value in the left middle cerebral artery (MCA) territory; (D) severe stenosis in the left proximal ICA (yellow arrow); (E-H) acute infarction in the left corona radiata and bilateral anterior cerebral artery territory on DWI, especially in the left anterior cingulate cortex (yellow arrow); (I, J) large but subtle DWI high-signal intensity in the left MCA territory (yellow dashed lines); (K) occlusion of the left MCA inferior division (yellow arrow) on conventional angiography; (L) subsequent recanalization after immediate mechanical thrombectomy.

Reference

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