Andrew Southerland, Gustavo Rohde, Mark McDonald, Omar Uribe, Yan Zhuang
Early recognition and treatment of stroke is critical in improving chances of recovery and decreasing the risk for long-term disability. Detecting stroke can be a difficult task as there are no objective measures to determine the presence of stroke signs, and current screening tools heavily rely on visual recognition which varies widely with experience and training.
NeuroView is focused on improving stroke detection by implementing an automated system to reduce errors during screening and triage of patients. We are creating algorithms that analyze a patient video feed from the cameras found on any mobile device and recognize signs of stroke in real-time. Our software will provide emergency medical personnel with objective data to more accurately determine the presence or absence of stroke. Increased recognition of stroke will allow emergency personnel to make better triage decisions, getting patients to the appropriate hospital faster and resulting in improved outcomes.