Stroke is a major cause of mortality and disability worldwide from which one in four people are in danger of incurring in their lifetime. The pre-hospital stroke assessment plays a vital role in identifying stroke patients accurately to accelerate further examination and treatment in hospitals. Accordingly, the National Institutes of Health Stroke Scale (NIHSS), Cincinnati Pre-hospital Stroke Scale (CPSS) and Face Arm Speed Time (F.A.S.T.) are globally known tests for stroke assessment. However, the validity of these tests is skeptical in the absence of neurologists and access to healthcare may be limited. Therefore, in this study, we propose a motion-aware and multi-attention fusion network (MAMAF-Net) that can detect stroke from multimodal examination videos. Contrary to other studies on stroke detection from video analysis, our study for the first time proposes an end-to-end solution from multiple video recordings of each subject with a dataset encapsulating stroke, transient ischemic attack (TIA), and healthy controls. The proposed MAMAF-Net consists of motion-aware modules to sense the mobility of patients, attention modules to fuse the multi-input video data, and 3D convolutional layers to perform diagnosis from the attention-based extracted features. Experimental results over the collected Stroke-data dataset show that the proposed MAMAF-Net achieves a successful detection of stroke with 93.62% sensitivity and 95.33% AUC score.

翻译：卒中是全球导致死亡和残疾的主要原因，每四人中便有一人一生中面临罹患该疾病的风险。院前卒中评估对准确识别卒中患者、加速医院后续检查与治疗至关重要。为此，美国国立卫生研究院卒中量表（NIHSS）、辛辛那提院前卒中量表（CPSS）及“面部-手臂-言语-时间”（F.A.S.T.）测试成为全球公认的卒中评估手段。然而，在缺乏神经科医生或医疗资源受限的情况下，这些测试的有效性存疑。因此，本研究提出一种运动感知与多注意力融合网络（MAMAF-Net），能够从多模态检查视频中检测卒中。与现有基于视频分析的卒中检测研究不同，本研究首次提出一种端到端解决方案，利用包含卒中、短暂性脑缺血发作（TIA）及健康对照者的多段视频数据集进行建模。所提出的MAMAF-Net包含：感知患者活动能力的运动感知模块、融合多输入视频数据的注意力模块，以及基于注意力提取特征进行诊断的3D卷积层。在收集的Stroke-data数据集上的实验结果表明，所提出的MAMAF-Net实现了93.62%的敏感性和95.33%的AUC评分，成功完成卒中检测。