Atherosclerotic Plaques Recognition in Intracoronary Optical Images Using Neural Networks

Abstract

Coronary artery disease (CAD) is intrinsically related to presence of atherosclerotic plaques. The rupture of this plaques is responsible for most acute coronary events. Intracoronary optical coherence tomography (IOCT) enables a detailed high-resolution visualization of micro-structural changes of the arterial wall in vivo. In this paper, we introduce a new way of identifying atherosclerotic plaques using 1D Convolutional Neural Networks (CNN) analyzing only the lumen contour. Training and test were performed with 1600 IOCT frames from in vivo patients. In our tests, we achieved f1-score of 95% for atherosclerotic plaque recognition. The results allow us to report an interesting correlation between the lumen contour geometry and the presence of plaques in the vascular wall observed through IOCT exams. The use of lumen contour for plaque detection opens two new perspectives: assisting specialists in the task of detecting plaques visually by paying special attention to the lumen and allowing methods to work in real time to detect plaques using efficient methods that use less information and deliver accurate results.