Abstract：
With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.
摘要：
随着近年来心血管成像领域的巨大进步,计算机断层扫描（CT）已成为动脉粥样硬化性冠状动脉疾病的表型。使用人工智能（AI）的新分析方法可以分析动脉粥样硬化斑块的复杂表型信息。特别是,使用卷积神经网络(CNN)的基于深度学习的方法促进了病变检测等任务，分割，和分类。新的放射转录组学技术甚至通过对CT图像上的体素进行高阶结构分析来捕获潜在的生物组织化学过程。在不久的将来,国际大规模牛津危险因素和非侵入性成像（ORFAN）研究将为测试和验证基于AI的预后模型提供强大的平台。目标是将这些新方法从研究环境转变为临床工作流程。在这次审查中,我们概述了现有的基于AI的技术，重点是成像生物标志物以确定冠状动脉炎症的程度，冠状动脉斑块，以及相关风险。Further,将讨论使用基于AI的方法的当前限制以及解决这些挑战的优先事项。这将为AI启用的风险评估工具铺平道路，以检测易损的动脉粥样硬化斑块并指导患者的治疗策略。
