In this talk we will discuss why and how deep learning approaches have the potential to greatly impact cardiac imaging. We will then explore use cases developed here at Columbia that have led to two of the world’s first prospective clinical trials of deep learning in cardiology. Lastly we’ll critique the limitations of current ML approaches preventing mainstream adoption in order to answer the question, “What are the big problems the field needs to be tackling now?” (and maybe even answer, “What’s a really good idea for me to do research on as a grad student?”)
Bio: Pierre Elias, MD is a cardiology fellow at Columbia University Irving Medical Center who recently completed a two-year postdoc in the Perotte Lab at DBMI. Prior to residency he was a data scientist at Lumiata, helping develop Google’s Knowledge Graph for Health. His research focuses on deep learning using cardiac imaging. He was recently named a STAT News Wunderkind, highlighting 20 of the most promising junior researchers around the country.
