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. 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 StrokeDATA dataset show that the proposed MAMAF-Net achieves a successful detection of stroke with 93.62% sensitivity and 95.33% AUC score.

翻译：中风是全球范围内导致死亡和残疾的主要原因之一，每四人中便有一人在一生中面临患病风险。院前中风评估在准确识别中风患者、加速医院后续检查与治疗中起着关键作用。为此，美国国立卫生研究院中风量表(NIHSS)、辛辛那提院前中风量表(CPSS)以及"面部手臂言语时间测试"(F.A.S.T.)是全球公认的中风评估标准。然而，在缺乏神经科医生的情况下，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)，其可通过多模态检查视频检测中风。与现有基于视频分析的中风检测研究不同，本研究首次提出一种端到端解决方案，基于包含中风、短暂性脑缺血发作(TIA)及健康对照者的数据集，对每位受试者的多段视频记录进行分析。所提出的MAMAF-Net包含运动感知模块以感知患者活动能力、注意力模块以融合多输入视频数据，以及3D卷积层以从基于注意力的提取特征中进行诊断。在收集的StrokeDATA数据集上的实验结果表明，该MAMAF-Net实现了中风检测的93.62%灵敏度和95.33%的AUC分数。