Keywords
imaging
AI in healthcare
How to Cite
Abstract
Intravascular optical coherence tomography (IVOCT) is an imaging system that is used in interventional cardiology to diagnose coronary atherosclerosis. It is a tool that produces high-resolution images that are used to visualise the microstructure of the coronary arteries. However, while IVOCT produces clear and sharp images to be used in diagnoses, the data produced are complex and voluminous, requiring large amounts of time and human effort to interpret. Recently, IVOCT has seen vast improvements and important advances. The objective of this literature review is to provide a critical analysis of the integration of artificial intelligence (AI) into IVOCT imaging, explore the achievements made, highlight the challenges faced, and map out the future trajectory of this technology. To fulfil this objective, several scientific studies and peer-reviewed journal articles were critically reviewed to illuminate the recent advances in AI-driven IVOCT imaging. The review focuses on addressing image pre-processing, segmentation, stent detection, plaque characterisation, stent malapposition and neointimal coverage assessment. Special considerations have also been given to the practical applications of these systems in clinical situations, especially in the South African low-resource context. The integration of AI into IVOCT imaging has powerful potential to increase the efficiency of image analysis, diagnostic accuracy, clinical reasoning and decision-making processes, thus forging a new and optimistic path for the future of cardiovascular care and precision medicine.
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