A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging

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PROJECT SUMMARY/ABSTRACT 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. This proposal will develop machine learning methods to create a physiologically grounded method for predicting TSS based on multiparametric magnetic resonance (MR) and computed tomography (CT) imaging data. We believe our proposed techniques will outperform state-of-the-art methods that are based on subjective image interpretation, and have the potential to provide an objective data point that may be used in conjunction with the subjective assessments of experts, or in clinical environments that lack expertise in stroke imaging Research has established that MR and CT imaging captures information that correlates with TSS. However, existing methods for extracting this information are based on a physician subjectively interpreting the images and delineating regions of interest, processes that have been documented to have only weak to moderate agreement across trained expert reviewers. An automated approach that comprehensively analyzes the spectrum of imaging data could identify complex relationships across channels that more accurately classify TSS. For example, in MR, diffusion-weighted, perfusion-weighted, and fluid attenuated inversion recovery imaging all play important roles in characterizing a stroke, but a deep understanding of how each channel may be combined to describe TSS is unknown. We propose to establish new deep learning methods for fusing this information. Specifically, we will: 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. Under this project, we will use data from the UCLA and UCI Stroke Centers, allowing us to study different patient populations and imaging techniques. The successful completion of this research will provide a new method for estimating TSS from imaging, leading to new prospective trials for providing therapy to patients with unknown TSS.