A topical review of the feasibility and reliability of ambulance-based telestroke

BackgroundAmbulance-based telemedicine is an innovative strategy through which transport time can be used to rapidly and accurately triage stroke patients (i.e., mobile telestroke). The acute phase of stroke is a time-sensitive emergency, and delays in care during this phase worsen outcomes. In this literature review, we analyzed studies that investigated the feasibility and reliability of ambulance based telestroke.MethodsWe followed PRISMA guidelines to perform a keyword-based search of PubMed, Web of Science, CINHAL, and Academic Search Complete databases. We reviewed references of search-identified articles to screen for additional articles. Articles for inclusion were selected according to author consensus in consideration of the studies' investigation of feasibility, reliability, or validity of ambulance-based telestroke.ResultsWe identified 67 articles for secondary screening from which 19 articles were selected for full text review. The selected studies reported diverse methods of development, implementation, and assessment of ambulance-based telestroke systems. Although the methods and results varied among these studies, most concluded that the implementation of ambulance based telestroke is feasible.ConclusionThis topical review suggests that ambulance based telestroke is a feasible method for enhanced prehospital stroke care in a variety of settings. Further prospective research is needed to assess the real-world challenges and to identify additional strategies that bolster rapid and accurate prehospital assessment of acute stroke patients

Human vs. Machine Learning Based Detection of Facial Weakness Using Video Analysis.

BACKGROUND: Current EMS stroke screening tools facilitate early detection and triage, but the tools\u27 accuracy and reliability are limited and highly variable. An automated stroke screening tool could improve stroke outcomes by facilitating more accurate prehospital diagnosis and delivery. We hypothesize that a machine learning algorithm using video analysis can detect common signs of stroke. As a proof-of-concept study, we trained a computer algorithm to detect presence and laterality of facial weakness in publically available videos with comparable accuracy, sensitivity, and specificity to paramedics. METHODS AND RESULTS: We curated videos of people with unilateral facial weakness ( CONCLUSIONS: These preliminary results suggest that a machine learning algorithm using computer vision analysis can detect unilateral facial weakness in pre-recorded videos with an accuracy and sensitivity comparable to trained paramedics. Further research is warranted to pursue the concept of augmented facial weakness detection and external validation of this algorithm in independent data sets and prospective patient encounters