Project Summary/Abstract Nearly one million Americans suffer from a stroke every year, often resulting in serious long-term disability or death. Early detection and treatment of stroke is critical to improving patient outcomes. Several tools have previously been developed that attempt to screen and help identify patients having a stroke, but there are still many stroke cases that are missed, even after the patient arrives to the emergency room. This study aims to improve the identification and triage of stroke patients in emergency departments using artificial intelligence (AI) on multimedia patient data. Our hypothesis is that real-time, standardized, and reproducible stroke assessment tools using AI can improve stroke triage and decrease missed diagnoses in patients with minor-to-moderate neurological symptoms. The study plans to develop a DeepStroke+ augmented-intelligence framework to triage any kind of strokes (ischemic stroke, hemorrhagic stroke, transient ischemic attack, etc) versus stroke mimics commonly seen in emergency settings. We developed a DeepStroke+ framework for stroke triage using facial videos of English-speaking patients who are describing the “Cookie Theft” picture from the Boston Diagnostic Aphasia Examination. Based on this technology, we will develop a stroke triage app and validate it in different triage scenarios from mobile stroke units to emergency room triage in local and telestroke scenarios.

Project Narrative The proposed use of DeepStroke+ in this study aims to improve the identification and triage of stroke patients in emergency departments, with the goal of alleviating some of the burden on emergency room doctors in the United States. By using artificial intelligence (AI) to identify stroke patients quickly and accurately, medical personnel may be able to mobilize a stroke team and reduce the time to complete a physician evaluation for patients who arrive to the emergency room with treatable strokes. AI tools could help to streamline the triage process from mobile stroke units to hospital emergency rooms and potentially reduce the number of patients that mistakenly identified as acute stroke and reduce the operating cost of mobile stroke units. Additionally, they can be used 24/7 and may help to bridge the gap when there is a shortage of specialists in remote emergency room.