Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.