Pinna Alessandro, Boi Alberto, Mannelli Lorenzo, Balestrieri Antonella, Sanfilippo Roberto, Suri Jasjit, Saba Luca
Department of Radiology, University of Cagliari, 09124 Cagliari, Italy.
Department of Cardiology, Azienda Ospedaliera G. Brotzu, 09124 Cagliari, Italy.
Diagnostics (Basel). 2025 Jul 19;15(14):1822. doi: 10.3390/diagnostics15141822.
Coronary plaque vulnerability, more than luminal stenosis, drives acute coronary syndromes. Optical coherence tomography (OCT), intravascular ultrasound (IVUS), and coronary computed tomography angiography (CCTA) visualize plaque morphology in vivo, but manual interpretation is time-consuming and operator-dependent. We performed a narrative literature survey of artificial intelligence (AI) applications-focusing on machine learning (ML) architectures-for automated coronary plaque segmentation and risk characterization across OCT, IVUS, and CCTA. Recent ML models achieve expert-level lumen and plaque segmentation, reliably detecting features linked to vulnerability such as a lipid-rich necrotic core, calcification, positive remodelling, and a napkin-ring sign. Integrative radiomic and multimodal frameworks further improve prognostic stratification for major adverse cardiac events. Nonetheless, progress is constrained by small, single-centre datasets, heterogeneous validation metrics, and limited model interpretability. AI-enhanced plaque assessment offers rapid, reproducible, and comprehensive coronary imaging analysis. Future work should prioritize large multicentre repositories, explainable architectures, and prospective outcome-oriented validation to enable routine clinical adoption.
冠状动脉斑块易损性而非管腔狭窄是急性冠状动脉综合征的驱动因素。光学相干断层扫描（OCT）、血管内超声（IVUS）和冠状动脉计算机断层扫描血管造影（CCTA）可在体内可视化斑块形态，但人工解读耗时且依赖操作者。我们对人工智能（AI）应用进行了叙述性文献综述，重点关注机器学习（ML）架构，以实现对OCT、IVUS和CCTA图像的冠状动脉斑块自动分割和风险特征分析。最近的ML模型实现了专家级的管腔和斑块分割，能够可靠地检测与易损性相关的特征，如富含脂质的坏死核心、钙化、正性重构和餐巾环征。综合的放射组学和多模态框架进一步改善了主要不良心脏事件的预后分层。尽管如此，进展受到小样本、单中心数据集、异质验证指标和有限的模型可解释性的限制。AI增强的斑块评估提供了快速、可重复且全面的冠状动脉成像分析。未来的工作应优先建立大型多中心数据库、采用可解释的架构，并进行以预期结果为导向的前瞻性验证，以实现临床常规应用。
