DeepCADD: a deep neural network for automatic detection of coronary artery disease

Abstract

CONTEXT: Cardiovascular diseases represent the number one cause of death globally, which include the most common disorders in the heart’s health, namely coronary artery disease (CAD). CAD is mainly caused by fat accumulated in the arteries’ internal walls, creating an atherosclerotic plaque that impacts the functional behavior of the blood flow. Anatomical plaque characteristics are essential for a complete functional assessment of CAD. In fact, there is no unique method to assess all the coronary artery segments with high accuracy. OBJECTIVE: Such a panorama evidences the need for new techniques applied to image exams to improve the functional assessment of cardiovascular diseases by replacing manual activities with an automated segment selection. METHODOLOGY: This study presents a deep object detection neural network architecture, called DeepCADD to determine the lesion location in right coronary arteries (RCA) angiography exams. Using a Mask Region-Based Convolutional Neural Network (R-CNN), we expect to reach precision comparable to the gold standard, automating one step of the current protocol. We replace the Mask R-CNN’s backbone with a ResNet-50 trained with coronary artery segments to improve the small features detection. We also train the whole DeepCADD architecture with angiographies collected in a local institution. RESULTS: DeepCADD outperformed similar networks in terms of sensitivity and presented a significant correlation with specialists during the validation, which suggests that DeepCADD can be used in the current angiography protocol. CONCLUSION: DeepCADD increases the correlation between the specialists and provides visual CAD suggestions, specially in multi-vessel lesions, which differentiates DeepCADD from the current literature. DeepCADD detects a high number of true positive candidates for lesion quantification, which we expect to extend for further arteries and dynamic evaluation in future research.