Chandramohan Nitin, Hinton Jonathan, O'Kane Peter, Johnson Thomas W
Translational Health Sciences, University of Bristol Bristol, UK.
University Hospitals Dorset NHS Foundation Trust Poole, UK.
Interv Cardiol. 2024 Mar 11;19:e03. doi: 10.15420/icr.2023.13. eCollection 2024.
Intravascular optical coherence tomography (IVOCT) is a form of intra-coronary imaging that uses near-infrared light to generate high-resolution, cross-sectional, and 3D volumetric images of the vessel. Given its high spatial resolution, IVOCT is well-placed to characterise coronary plaques and aid with decision-making during percutaneous coronary intervention. IVOCT requires significant interpretation skills, which themselves require extensive education and training for effective utilisation, and this would appear to be the biggest barrier to its widespread adoption. Various artificial intelligence-based tools have been utilised in the most contemporary clinical IVOCT systems to facilitate better human interaction, interpretation and decision-making. The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.
血管内光学相干断层扫描（IVOCT）是一种冠状动脉内成像形式，它使用近红外光生成血管的高分辨率、横截面和三维容积图像。鉴于其高空间分辨率，IVOCT非常适合用于表征冠状动脉斑块，并在经皮冠状动脉介入治疗期间辅助决策。IVOCT需要高超的解读技能，而这些技能本身需要广泛的教育和培训才能有效运用，这似乎是其广泛应用的最大障碍。在最现代的临床IVOCT系统中，已使用了各种基于人工智能的工具，以促进更好的人机交互、解读和决策。本文的目的是回顾IVOCT的现有和未来技术发展，并展示它们如何帮助操作人员。
