The goal of this projetc is to classify the blood clot origins in ischemic stroke. Using whole slide digital pathology images, i build a model that differentiates between the two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis.
My work will enable healthcare providers to better identify the origins of blood clots in deadly strokes, making it easier for physicians to prescribe the best post-stroke therapeutic management and reducing the likelihood of a second stroke.
Stroke remains the second-leading cause of death worldwide. Each year in the United States, over 700,000 individuals experience an ischemic stroke caused by a blood clot blocking an artery to the brain. A second stroke (23% of total events are recurrent) worsens the chances of the patient’s survival. However, subsequent strokes may be mitigated if physicians can determine stroke etiology, which influences the therapeutic management following stroke events.
During the last decade, mechanical thrombectomy has become the standard of care treatment for acute ischemic stroke from large vessel occlusion. As a result, retrieved clots became amenable to analysis. Healthcare professionals are currently attempting to apply deep learning-based methods to predict ischemic stroke etiology and clot origin. However, unique data formats, image file sizes, as well as the number of available pathology slides create challenges you could lend a hand in solving.
The Mayo Clinic is a nonprofit American academic medical center focused on integrated health care, education, and research. Stroke Thromboembolism Registry of Imaging and Pathology (STRIP) is a uniquely large multicenter project led by Mayo Clinic Neurovascular Lab with the aim of histopathologic characterization of thromboemboli of various etiologies and examining clot composition and its relation to mechanical thrombectomy revascularization.
To decrease the chances of subsequent strokes, the Mayo Clinic Neurovascular Research Laboratory encourages data scientists to improve artificial intelligence-based etiology classification so that physicians are better equipped to prescribe the correct treatment. New computational and artificial intelligence approaches could help save the lives of stroke survivors and help us better understand the world's second-leading cause of death.