Improvement of automated analysis of coronary Doppler echocardiograms

Abstract

Coronary artery disease is the leading cause of heart disease, 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 coronary 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 and heuristics by (1) moving the program to a Python environment, (2) increasing the program’s ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases.

Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment

“…Previous studies had used computer algorithms to automate and optimize Doppler signal processing in a mouse model and in transthoracic Doppler sonography of the human coronary artery. 23–25 These studies tended to focus on the use of computer algorithm–based automation rather than machine learning. Although these methods are potentially useful, they do not benefit from the advantages that neural networks offer in terms of approximating human expert discrimination in both image quality and envelope tracking.…”