AI RESEARCH
Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening
arXiv CS.CV
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ArXi:2601.11896v2 Announce Type: replace Early identification of stroke symptoms is essential for enabling timely intervention and improving patient outcomes, particularly in prehospital settings. This study presents a fast, non-invasive multimodal deep learning framework for automatic binary stroke screening based on data collected during the F. A. S. T. assessment. The proposed approach integrates complementary information from facial expressions, speech signals, and upper-body movements to enhance diagnostic robustness.