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According to the WHO, Cerebrovascular Stroke, or CS, is the second largestcause of death worldwide. Current diagnosis of CS relies on labor and costintensive neuroimaging techniques, unsuitable for areas with inadequate accessto quality medical facilities. Thus, there is a great need for an efficientdiagnosis alternative. StrokeSave is a platform for users to self-diagnose forprevalence to stroke. The mobile app is continuously updated with heart rate,blood pressure, and blood oxygen data from sensors on the patient wrist. Oncethese measurements reach a threshold for possible stroke, the patient takesfacial images and vocal recordings to screen for paralysis attributed tostroke. A custom designed lens attached to a phone's camera then takes retinalimages for the deep learning model to classify based on presence of retinopathyand sends a comprehensive diagnosis. The deep learning model, which consists ofa RNN trained on 100 voice slurred audio files, a SVM trained on 410 vasculardata points, and a CNN trained on 520 retinopathy images, achieved a holisticaccuracy of 95.0 percent when validated on 327 samples. This value exceeds thatof clinical examination accuracy, which is around 40 to 89 percent, furtherdemonstrating the vital utility of such a medical device. Through thisautomated platform, users receive efficient, highly accurate diagnosis withoutprofessional medical assistance, revolutionizing medical diagnosis of CS andpotentially saving millions of lives.

StrokeSave: A Novel, High-Performance Mobile Application for Stroke Diagnosis using Deep Learning and Computer Vision