Percutaneous coronary intervention (PCI) remains the cornerstone of revascularization in coronary artery disease. However, despite advances in stent technology, adverse outcomes related to suboptimal stent placement including under-expansion, malapposition, incomplete coverage, edge dissection, and neoatherosclerosis persist. Optical coherence tomography (OCT) is the highest resolution widely available intravascular imaging modality, allowing unparalleled visualization of stent-vessel interactions and plaque morphology. However, there are significant barriers to large-scale OCT adoption, including time-consuming manual interpretation and dependence on the operator, resulting in significant variability between analysts. Artificial intelligence (AI)-based automated OCT analysis promises to assist PCI by delivering rapid, reproducible, and standardized stent optimization, but current analysis systems lack important features to optimize stent placement. This review summarizes the principles of OCT in PCI, the evidence for OCT-guided optimization, and recent advances in AI-driven automated OCT analysis. We discuss plaque analysis and classification, highlighting how AI-driven automated OCT analysis can identify fibrous, lipid-rich, and calcified lesions and guide tailored lesion preparation using balloons, specialty balloons, drug-coated balloons, intravascular lithotripsy, and atherectomy. We outline emerging AI-enabled workflows for fully automated PCI optimization, evaluate clinical outcomes, interpretability, and workflow integration. Finally, we explore future directions including AI imaging-physiology fusion, multiomics integration, and robotics. If successful, automated AI-empowered OCT analysis could become a central imaging platform for precision PCI, enabling tailored lesion preparation, optimal stent deployment, and improved clinical outcomes in coronary artery disease management.
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Mohee et al. (2026) studied this question.
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