Machine learning offers great potential for automated prediction of
post-stroke symptoms and their response to rehabilitation. Major challenges for
this endeavour include the very high dimensionality of neuroimaging data, the
relatively small size of the datasets available for learning, and how to
effectively combine neuroimaging and tabular data (e.g. demographic information
and clinical characteristics). This paper evaluates several solutions based on
two strategies. The first is to use 2D images that summarise MRI scans. The
second is to select key features that improve classification accuracy.
Additionally, we introduce the novel approach of training a convolutional
neural network (CNN) on images that combine regions-of-interest extracted from
MRIs, with symbolic representations of tabular data. We evaluate a series of
CNN architectures (both 2D and a 3D) that are trained on different
representations of MRI and tabular data, to predict whether a composite measure
of post-stroke spoken picture description ability is in the aphasic or
non-aphasic range. MRI and tabular data were acquired from 758 English speaking
stroke survivors who participated in the PLORAS study. The classification
accuracy for a baseline logistic regression was 0.678 for lesion size alone,
rising to 0.757 and 0.813 when initial symptom severity and recovery time were
successively added. The highest classification accuracy 0.854 was observed when
8 regions-of-interest was extracted from each MRI scan and combined with lesion
size, initial severity and recovery time in a 2D Residual Neural Network.Our
findings demonstrate how imaging and tabular data can be combined for high
post-stroke classification accuracy, even when the dataset is small in machine
learning terms. We conclude by proposing how the current models could be
improved to achieve even higher levels of accuracy using images from hospital
scanners.

机器学习在预测中风后症状及其对康复的反应方面具有巨大潜力。本研究评估了对神经影像数据进行维度降低和特征选择的方法，并提出了使用卷积神经网络 (CNN) 对结合神经影像和表格数据的新方法。研究结果显示，即使数据集规模较小，结合神经影像和表格数据可实现高精度的中风后分类，可以进一步通过使用医院扫描仪中的图像来提高分类准确性。