Shrivastava Priyal, Kashikar Shivali, Parihar P H, Kasat Pachyanti, Bhangale Paritosh, Shrivastava Prakher
Department of Radio-Diagnosis, Jawaharlal Nehru Medical College Wardha, Datta Meghe Institute of Higher Education and Research (DU), Sawangi (Meghe), Wardha, India.
Jawaharlal Nehru Medical College Wardha, Datta Meghe Institute of Higher Education and Research (DU), Sawangi (Meghe), Wardha, India.
Eur J Radiol Open. 2025 May 2;14:100652. doi: 10.1016/j.ejro.2025.100652. eCollection 2025 Jun.
Coronary artery disease (CAD) is a major worldwide health concern, contributing significantly to the global burden of cardiovascular diseases (CVDs). According to the 2023 World Health Organization (WHO) report, CVDs account for approximately 17.9 million deaths annually. This emphasizies the need for advanced diagnostic tools such as coronary computed tomography angiography (CCTA). The incorporation of deep learning (DL) technologies could significantly improve CCTA analysis by automating the quantification of plaque and stenosis, thus enhancing the precision of cardiac risk assessments. A recent meta-analysis highlights the evolving role of CCTA in patient management, showing that CCTA-guided diagnosis and management reduced adverse cardiac events and improved event-free survival in patients with stable and acute coronary syndromes.
An extensive literature search was carried out across various electronic databases, such as MEDLINE, Embase, and the Cochrane Library. This search utilized a specific strategy that included both Medical Subject Headings (MeSH) terms and pertinent keywords. The review adhered to PRISMA guidelines and focused on studies published between 2019 and 2024 that employed deep learning (DL) for coronary computed tomography angiography (CCTA) in patients aged 18 years or older. After implementing specific inclusion and exclusion criteria, a total of 10 articles were selected for systematic evaluation regarding quality and bias.
This systematic review included a total of 10 studies, demonstrating the high diagnostic performance and predictive capabilities of various deep learning models compared to different imaging modalities. This analysis highlights the effectiveness of these models in enhancing diagnostic accuracy in imaging techniques. Notably, strong correlations were observed between DL-derived measurements and intravascular ultrasound findings, enhancing clinical decision-making and risk stratification for CAD.
Deep learning-enabled CCTA represents a promising advancement in the quantification of coronary plaques and stenosis, facilitating improved cardiac risk prediction and enhancing clinical workflow efficiency. Despite variability in study designs and potential biases, the findings support the integration of DL technologies into routine clinical practice for better patient outcomes in CAD management.
冠状动脉疾病（CAD）是全球主要的健康问题，对全球心血管疾病（CVD）负担有重大影响。根据2023年世界卫生组织（WHO）报告，CVD每年导致约1790万人死亡。这凸显了对先进诊断工具的需求，如冠状动脉计算机断层扫描血管造影（CCTA）。深度学习（DL）技术的融入可以通过自动量化斑块和狭窄程度显著改善CCTA分析，从而提高心脏风险评估的准确性。最近的一项荟萃分析强调了CCTA在患者管理中不断演变的作用，表明CCTA引导的诊断和管理可减少稳定型和急性冠状动脉综合征患者的不良心脏事件，并改善无事件生存率。
在多个电子数据库中进行了广泛的文献检索，如MEDLINE、Embase和Cochrane图书馆。该检索采用了一种特定策略，包括医学主题词（MeSH）术语和相关关键词。该综述遵循PRISMA指南，重点关注2019年至2024年期间发表的、对18岁及以上患者使用深度学习（DL）进行冠状动脉计算机断层扫描血管造影（CCTA）的研究。在实施特定的纳入和排除标准后，共选择了10篇文章进行质量和偏倚的系统评价。
该系统评价共纳入10项研究，与不同成像方式相比，展示了各种深度学习模型的高诊断性能和预测能力。该分析突出了这些模型在提高成像技术诊断准确性方面的有效性。值得注意的是，在DL衍生测量值与血管内超声结果之间观察到强相关性，增强了CAD的临床决策和风险分层。
基于深度学习的CCTA在冠状动脉斑块和狭窄量化方面代表了一项有前景的进展，有助于改善心脏风险预测并提高临床工作流程效率。尽管研究设计存在差异和潜在偏倚，但研究结果支持将DL技术整合到常规临床实践中，以在CAD管理中实现更好的患者预后。
