High-Throughput Precision Identification of Cardiac Amyloidosis in a Diverse Population.

Video Recording

Speaker

David Ouyang, MD, Cedars-Sinai Medical Center

Dr. Ouyang is a physician-scientist and cardiologist with subspecialty expertise in non-invasive imaging and technical background in programming, statistics, and deep learning. As a statistician by training, he has always been fascinated by how to understand, visualize, and interpret data. With the exponential rise in the amount of data being collected in clinical care, there are huge opportunities to apply the additional data to personalize care and improve diagnosis and treatment of cardiovascular disease. Ouyang’s goal as an investigator is to have expertise in echocardiography and deep learning applied to medical imaging, while leveraging clinically generated data to inform scientific discovery. His previous work has demonstrated the ability of convolutional neural networks to identify systemic human phenotypes that modify cardiovascular risk previously thought to be not apparent on echocardiographic images and precision phenotyping of cardiac function with higher precision than human interpreters. As an advanced cardiac imager and cardiologist, Dr. Ouyang hopes to advance and enhance cardiovascular imaging.

Featured Discussant

Rohan Khera, MD, MS, Yale School of Medicine

Assistant Professor of Medicine (Cardiovascular Medicine) and of Biostatistics (Health Informatics); Clinical Director, Center for Health Informatics and Analytics, YNHH/Yale CORE; Director, Cardiovascular Data Science Lab (CarDS)

Dr. Khera is a Cardiologist and Data Scientist and the Director of the Cardiovascular Data Science (CarDS) Lab, which is a multidisciplinary group focusing on data-driven discovery in cardiovascular disease. He is the Clinical Director of the Center for Health Informatics and Analytics at the Yale Center for Outcomes Research and Evaluation. He is also an Associate Editor for Artificial Intelligence and Digital Health at JAMA.

The CarDS Lab, which Dr. Khera leads, is developing and implementing strategies to improve outcomes for patients with or at risk for cardiovascular disease through data-driven innovations in delivering evidence-based, patient-centered care. Dr. Khera’s work focuses on novel applications in medical informatics, machine learning, and artificial intelligence to evaluate patient care and develop precision care solutions. His work spans broad digital data sources, including electronic health records, electrocardiography, cardiovascular imaging, and wearable devices, with applications to modernize US and global healthcare. The work in his Lab is supported by grants from the National Institutes of Health and the Doris Duke Charitable Foundation

Session Description: This session will focus on the progress of the project led by Dr, Ouyang “High-Throughput Precision Identification of Cardiac Amyloidosis in a Diverse Population”. This project evaluates the implementation of a machine-learning based opportunistic screening algorithm to help detect an underdiagnosed cardiac amyloidosis (CA), a rare fatal disease especially underdiagnosed among Black patients and the elderly. This project will apply a previously validated and published algorithm to existing echocardiogram images in an automated screening workflow to identify those at highest suspicion for cardiac amyloidosis for downstream evaluation by specialists. In addition to deploying the AI tool, the project will evaluate if the screening program is effective at identifying patients with cardiac amyloidosis, improves health outcomes, and is embraced by clinicians in the health system.

The session will conclude with a discussion led by Rohan Khera, MD, MS, Yale School of Medicine