  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.
