PI: Rama Chellappa, PhD (Bloomberg Distinguished Professor, Department of Electrical and Computer Engineering, Department of Biomedical Engineering)
Co-I: Robert D. Stevens, MD
Neurological outcomes of ischemic stroke (IS) have substantially improved due to advances in the available treatment options. However, these treatments are highly time-sensitive and are often delayed because symptoms may be quite variable and of uncertain significance, especially for untrained observers. We hypothesize that quantifiable abnormalities in facial expression, eye movements, and speech (phenotypic features) are detectable in all stroke patients, and that these features can be extracted using computational algorithms applied to smartphone video recordings of facial expression and speech. Our aim is to create a system for IS detection and severity assessment based on computational analysis of these phenotypic features. We also aim to develop a prognostic system to determine the clinical outcome of IS from phenotypic signals.
Prof. Rama Chellappa is a Bloomberg Distinguished Professor in the Department of Electrical and Computer Engineering in the Whiting School of Engineering and in the Department of Biomedical Engineering in the School of Medicine. His research interests are Computer Vision, Artificial Intelligence, Biomedical Data Sciences, and Machine Learning.
