Sperti Michela, Cardaci Camilla, Bruno Francesco, Shah Syed Taimoor Hussain, Panagiotopoulos Konstantinos, Kassem Karim, Nisco Giuseppe De, Morbiducci Umberto, Piccolo Raffaele, Burzotta Francesco, D'Ascenzo Fabrizio, Deriu Marco Agostino, Chiastra Claudio
Department of Mechanical and Aerospace Engineering, Polito Med Lab, Politecnico di Torino, 10129 Torino, Italy.
Division of Cardiology, Department of Medical Sciences, Città della Salute e della Scienza, University of Turin, 10126 Turin, Italy.
Rev Cardiovasc Med. 2025 Jul 29;26(7):39210. doi: 10.31083/RCM39210. eCollection 2025 Jul.
Intravascular optical coherence tomography (IVOCT) is emerging as an effective imaging technique for accurately characterizing coronary atherosclerotic plaques. This technique provides detailed information on plaque morphology and composition, enabling the identification of high-risk features associated with coronary artery disease and adverse cardiovascular events. However, despite advancements in imaging technology and image assessment, the adoption of IVOCT in clinical practice remains limited. Manual plaque assessment by experts is time-consuming, prone to errors, and affected by high inter-observer variability. To increase productivity, precision, and reproducibility, researchers are increasingly integrating artificial intelligence (AI)-based techniques into IVOCT analysis pipelines. Machine learning algorithms, trained on labelled datasets, have demonstrated robust classification of various plaque types. Deep learning models, particularly convolutional neural networks, further improve performance by enabling automatic feature extraction. This reduces the reliance on predefined criteria, which often require domain-specific expertise, and allow for more flexible and comprehensive plaque characterization. AI-driven approaches aim to facilitate the integration of IVOCT into routine clinical practice, potentially transforming this technique from a research tool into a powerful aid for clinical decision-making. This narrative review aims to (i) provide a comprehensive overview of AI-based methods for analyzing IVOCT images of coronary arteries, with a focus on plaque characterization, and (ii) explore the clinical translation of AI to IVOCT, highlighting AI-powered tools for plaque characterization currently intended for commercial and/or clinical use. While these technologies represent significant progress, current solutions remain limited in the range of plaque features these methods can assess. Additionally, many of these solutions are confined to specific regulatory or research settings. Therefore, this review highlights the need for further advancements in AI-based IVOCT analysis, emphasizing the importance of additional validation and improved integration with clinical systems to enhance plaque characterization, support clinical decision-making, and advance risk prediction.
血管内光学相干断层扫描（IVOCT）正逐渐成为一种用于准确表征冠状动脉粥样硬化斑块的有效成像技术。该技术可提供有关斑块形态和成分的详细信息，有助于识别与冠状动脉疾病及不良心血管事件相关的高危特征。然而，尽管成像技术和图像评估取得了进展，但IVOCT在临床实践中的应用仍然有限。专家进行的手动斑块评估耗时、容易出错，且受观察者间高变异性的影响。为了提高效率、精度和可重复性，研究人员越来越多地将基于人工智能（AI）的技术集成到IVOCT分析流程中。在标记数据集上训练的机器学习算法已证明能够对各种斑块类型进行可靠分类。深度学习模型，特别是卷积神经网络，通过实现自动特征提取进一步提高了性能。这减少了对通常需要特定领域专业知识的预定义标准的依赖，并允许进行更灵活、全面的斑块表征。AI驱动的方法旨在促进IVOCT融入常规临床实践，有可能将该技术从研究工具转变为临床决策的有力辅助手段。本叙述性综述旨在（i）全面概述用于分析冠状动脉IVOCT图像的基于AI的方法，重点是斑块表征，以及（ii）探讨AI在IVOCT中的临床转化，突出目前用于商业和/或临床用途的基于AI的斑块表征工具。虽然这些技术取得了重大进展，但当前的解决方案在这些方法能够评估的斑块特征范围方面仍然有限。此外，许多这些解决方案仅限于特定的监管或研究环境。因此，本综述强调了基于AI的IVOCT分析需要进一步进展，强调了额外验证以及与临床系统更好集成以增强斑块表征、支持临床决策和推进风险预测的重要性。
