BMIR Research Colloquium: Applications of Data Science and Machine Learning in Radiology and Cardiology

Speaker(s): 

Brandon Fornwalt MD, PhD

Free of charge; Lunch will be provided

Stanford Center for Biomedical Informatics Research
Research Colloquium presents:
“Applications of Data Science and Machine Learning in Radiology and Cardiology”
Brandon Fornwalt MD, PhD
Investigator II, Associate Professor
Geisinger Health System

Abstract:
The overall goal of our group is to leverage data-driven approaches to help improve patient outcomes. This talk will demonstrate examples of how we can work towards this goal by leveraging large clinical datasets, data science and machine learning. Specific examples include: 1) using 46,583 clinically-acquired 3D computed tomography images of the brain to develop and implement a deep learning model to efficiently reprioritize radiology worklists for quicker diagnosis of intracranial hemorrhage; 2) using deep learning to analyze 723,754 echocardiographic videos of the heart to accurately predict patient survival; 3) analyzing 2 million 12-lead electrocardiographic tracings from the heart to predict clinically relevant future events and 4) optimizing evidence-based care delivery for a population of >10,000 patients with heart failure using machine learning.

Bio:
Dr. Fornwalt attended the University of South Carolina as an undergraduate in mathematics and marine science. He then worked in a free medical clinic for a year before starting an MD/PhD program at Emory and Georgia Tech. After finishing his degrees in 2010, he completed an internship in pediatrics at Boston Children's Hospital before becoming an Assistant Professor at the University of Kentucky. After four years on faculty in Kentucky, Dr. Fornwalt moved to Geisinger where he founded Geisinger's Department of Imaging Science and Innovation and focuses on data-driven approaches to improving patient outcomes. Dr. Fornwalt is also a radiologist and a member of the Geisinger Heart Institute.