Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning.

ABSTRACT:

Background

Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography.

SUBMITTER: Stuckey TD 

PROVIDER: S-EPMC6082503 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature