A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging (R01 NS100806; PI: Corey Arnold)
Stroke is a leading cause of mortality and morbidity in the United States, with approximately 795,000 Americans experiencing a new or recurrent stroke each year. Intravenous tissue plasminogen activator (IV tPA) is the dominant and most proven treatment option, but its use is only indicated within 4.5 hours following a stroke. Unfortunately, up to 30% of stroke patients present with an unknown time since stroke (TSS) symptom onset, which makes them ineligible to receive IV tPA. Many of these individuals could be spared severe morbidity or mortality if there existed an alternative method for establishing TSS, allowing them to be identified and treated.
In this project, we aim to: 1) develop a machine learning framework for classifying TSS; 2) develop a deep convolutional autoencoder to generate novel multimodal image representations from MR and CT to improve classification; and 3) implement visualization techniques that elucidate the relationship between deep features and pathophysiological stroke processes