Thin-Cap Fibroatheroma Detection with Deep Neural Networks

Abstract

Acute coronary syndromes (ACS) frequently results in unstable angina, acute myocardial infarction, and sudden coronary death. The most of ACS are related to coronary thrombosis that mainly caused by plaque rupture followed by plaque erosion. Thin-cap fibroatheroma (TCFA) is a well-known type of vulnerable plaque which is prone to serious plaque rupture. Intravascular ultrasound (IVUS) is the most common methods for imaging coronary arteries to determine the amount of plaque built up at the epicardial coronary artery. However, since IVUS has relatively lower resolution than that of optical coherence tomography (OCT), TCFA detection with IVUS is considerably difficult. In this paper, we propose a novel method of TCFA detection with IVUS images using machine learning technique. 12,325 IVUS images from 100 different patients were labeled with equivalent frames from OCT images. Deep feed-forward neural network (FFNN) was applied to a different number of selected features based on the Fishers exact test. As a result, IVUS derived TCFA detection achieved 0.87 area under the curve (AUC) with 78.31% specificity and 79.02% sensitivity. Our experimental result indicates a new possibility for detection of TCFA with IVUS images using machine learning technique.