Abstract Coronary artery disease, a major global health burden, is characterized by the progressive buildup of plaques in the walls of coronary arteries, leading to restrictive narrowing of blood flow and potentially life-threatening complications. Intravascular optical coherence tomography (OCT) enables high-resolution assessment of plaque features, but image analysis remains time-consuming and operator-dependent. This review examines the application of deep learning (DL) to intravascular OCT for coronary plaque analysis, with the aim of clarifying the current scope of research, synthesizing methodological developments, and identifying the main barriers to clinical translation. We reviewed 80 original studies published between January 2017 and March 2025. The review covers the major methodological components of DL-based plaque analysis, including datasets, preprocessing, validation strategies, evaluation metrics, loss functions, model architectures for classification, detection, and segmentation, as well as post-processing and implementation practices. The literature shows a clear progression from the direct use of general-purpose computer vision models to increasingly specialized architectures and processing pipelines tailored to intravascular OCT data. This evolution also reveals a central tension: although increasing model complexity often improves predictive performance, clinically meaningful deployment still depends on stronger generalizability, robustness, interpretability, and reproducibility. By critically synthesizing these developments, this review provides a structured methodological reference for researchers, highlights the principal technical and translational limitations in the field, and outlines practical directions for developing more reliable and clinically applicable DL systems for intravascular imaging.
Chen et al. (Thu,) studied this question.