Radiomics approach to engineering an artificial intelligence based echocardiography platform to predict cardiovascular surgery and heart failure outcomes.

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SUMMARY In recent years, artificial intelligence has enabled automated systems to meet or exceed the performance of clinical experts across a wide variety of medical imaging tasks, in applications ranging from disease diagnosis using Chest X-Rays to survival analyses using histopathology slides. All current automated echocardiography systems – much like human echocardiography reads – are inherently reductionist in nature; a complex sequence and pattern of cardiac contraction is reduced to an outline of one or more chambers, from which a few global metrics of heart function are then calculated. Despite the staggering increase in usable data, the vast majority of information contained in time-resolved echocardiography videos remain woefully underutilized. As opposed to treating echocardiography studies as videos intended solely for visual interpretation, the ‘radiomics’ approach treats medical images as high-dimensional datasets to be mined with advanced computational tools. The overall goals of this project are to further develop and validate our novel, generalizable, multi-modal artificial intelligence (AI) platform for analyzing time resolved echocardiography studies, to address this underutilization. The impact of such an ECHO AI system is immediately perceptible in the field of heart failure. An estimated 6.5 million people suffer from heart failure in the United States. Across the spectrum of severity in this disease, echocardiography remains the cornerstone of screening and clinical diagnosis, a guide for medical management and pharmacotherapy, and an essential tool for planning acute lifesaving surgical interventions. We propose to build on our preliminary research and ready access to high quality paired echocardiographic and clinical datasets to achieve the following goals: 1) Develop a surgical decision support system for end-stage heart failure patients considered for left ventricular assist device (LVAD) implant. 2) Expand and generalize our ECHO AI tools to enable downstream prediction of long-term survival and development of heart failure, in both asymptomatic individuals and patients with pulmonary arterial hypertension 3) Cloud and hardware integration of our ECHO AI platform. The end result of our research will be a powerful ECHO AI tool with that is translatable, and integrated into clinical practice.