Jeong Soo, Lee Eun-Jae, Kim Yong-Hwan, Woo Jin Cheol, Ryu On-Wha, Kwon Miseon, Kwon Sun U, Kim Jong S, Kang Dong-Wha
Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Nunaps Inc., Seoul, Korea.
J Stroke. 2022 Jan;24(1):108-117. doi: 10.5853/jos.2021.02061. Epub 2022 Jan 31.
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.
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.
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).
DL approaches using DWI data may be feasible and useful for estimating the severity of aphasia in the early stage of stroke.
本研究旨在探讨使用扩散加权成像（DWI）数据的深度学习（DL）模型在急性卒中患者早期预测失语严重程度的适用性。
我们回顾性分析了2011年至2013年间在峨山医学中心就诊的、由左侧大脑中动脉区域急性缺血性卒中导致失语的连续患者。为了实施预测卒中后失语严重程度的DL模型，我们设计了一个深度前馈网络，利用DWI数据中的病变占位比，并建立临床变量来估计韩国版西方失语成套测验的失语商（AQ）评分（范围为0至100）。为了评估DL模型的性能，我们分析了分类AQ评分（0 - 25，极重度；26 - 50，重度；51 - 75，中度；≥76，轻度）的Cohen加权kappa系数和连续值的Pearson相关系数。
我们确定了225例卒中后失语患者，其中176例被纳入并进行分析。对于分类AQ评分，Cohen加权kappa系数为0.59（95%置信区间[CI]，0.42至0.76；P < 0.001）。对于连续AQ评分，真实AQ评分与模型估计值之间的相关系数为0.72（95% CI，0.55至0.83；P < 0.001）。
使用DWI数据的DL方法在估计卒中早期失语严重程度方面可能是可行且有用的。
