介入心脏病专家的人工智能:为 OCT 图像解读提供动力和支持

Nitin Chandramohan, Jonathan Hinton, Peter O’Kane, Thomas W Johnson
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引用次数: 0

摘要

血管内光学相干断层扫描(IVOCT)是一种冠状动脉内成像技术,它利用近红外线生成血管的高分辨率横截面和三维容积图像。IVOCT 具有很高的空间分辨率,因此非常适合描述冠状动脉斑块的特征,并有助于经皮冠状动脉介入治疗过程中的决策制定。IVOCT 需要大量的判读技能,这本身就需要大量的教育和培训才能有效利用,这似乎是其广泛应用的最大障碍。各种基于人工智能的工具已被用于最先进的临床 IVOCT 系统,以促进更好的人机交互、解读和决策。本文旨在回顾 IVOCT 现有和未来的技术发展,并展示这些技术如何为操作者提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation
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.
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