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Researchers utilized a smartphone app to detect early signs of stroke. Researchers gathered at the Society of NeuroInterventional Surgery's (SNIS) 20th Annual Meeting to discuss a smartphone app designed to detect early signs of stroke accurately, leveraging the power of machine learning.
Today at the Society of NeuroInterventional Surgery's (SNIS) 20th Annual Meeting, researchers discussed a smartphone app created that reliably recognizes patients' physical signs of stroke with the power of machine learning.
Today at the Society of NeuroInterventional Surgery’s (SNIS) 20th Annual Meeting, researchers discussed a smartphone app created that reliably recognizes patients’ physical signs of stroke with the power of machine learning. In the study, “Smartphone-Enabled Machine Learning Algorithms for Autonomous Stroke Detection,” researchers from the UCLA David Geffen School of Medicine and multiple medical institutions in Bulgaria used data from 240 patients with stroke at four metropolitan stroke centers. Within 72 hours of the start of the patients’ symptoms, researchers used smartphones to...