A conversation with Ramin Zand, MD, Neurology Director of Clinical Stroke Operations, Northeastern Regional Stroke Director, Geisinger Health System, and Associate Professor of Neurology, University of Tennessee Health Science Center, and Vida Abedi, PhD, Research Scientist, Geisinger Health System, and Adjunct Professor, Virginia Tech, about using an artificial neural network to screen for stroke.
Interviewed by José G. Merino, MD, Associate Professor of Neurology, University of Maryland School of Medicine.
They will be discussing the paper “Novel Screening Tool for Stroke Using Artificial Neural Network,” published in the June issue of Stroke.
Dr. Merino: Could you please briefly summarize the key findings and put them in context of what was known before you did the study (i.e. an “elevator pitch” about your research)?
Drs. Zand and Abedi: We have developed a new computational method based on artificial intelligence to screen for the stroke in an emergency setting. Previous studies have shown that up to 25% of strokes can be initially misdiagnosed in the emergency department. The failure to recognize stroke in the emergency department is a missed opportunity for intervention. The goal of our study was to test if a supervised learning method could recognize and differentiate stroke from stroke mimics based on the patient demographics, risk factors, and certain clinical elements. Our results showed that in 6 out of the 10 data sets, the precision of our tool for the diagnosis of stroke was >90%. We believe that these methods can serve as a clinical decision support system and assist the emergency providers with early recognition of stroke.