Jamie Bossenbroek – Computer Science Engineering

Project Title: “Improved Automated Analysis of Doppler Echocardiograms to predict Early Coronary Microvascular Disease”

Mentor: Will Ray, Pediatrics

Abstract:

Coronary microvascular disease is one of the earliest symptoms of serious heart conditions, and while it can be assessed through Transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze these flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles.  The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program’s accuracy. The goal of this current study was to refine the existing automation algorithm by 1) moving the program to a Python environment, 2) increasing the program’s ability to handle challenging video variations, and 3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners will be able to use the automatic program to easily and accurately identify the early signs of serious heart diseases before more life-threatening symptoms develop.

Narrated Research Poster: ResearchPoster

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