Hemorrhage Evaluation and Detector System for Underserved Populations: “HEADS UP”

Intracranial hemorrhage (ICH) is the second most common cause of stroke yet remains the second leading cause of disability globally and disproportionately deadlier than ischemic stroke. ICH also disproportionately impacts minorities and female mortality, particularly in rural and underserved areas of the globe. The noncontrast head CT (NCCT) remains the gold-standard way to differentiate ischemic stroke versus hemorrhagic stroke. Since 2015 there has been a paradigm change approach to ischemic stroke patients using AI-ML using NCCT and CT perfusion to select patients for large vessel occlusion thrombectomy to improve outcomes. ICH patients, however, lack such a paradigm change in the acute stroke systems-of-care to improve outcomes. Therefore, there is an unmet patient need to create an earlier ICH detection method to trigger accelerated systems of care that are linked to better downstream patient outcomes and interventions. The objective is to create a rapid, cloud-based, deployable machine learning (ML) method to detect ICH potentially across the Mayo Clinic enterprise to help patients and clinical teams and to create scientific and intellectual independence from third party vendors.