基于人工智能的医学影像学检查在动脉粥样硬化性狭窄定量评估的研究进展  

Research Progress of AI-Based Medical Imaging Examination in Quantitative Evaluation of Atherosclerotic Stenosis

作　　者：宦欣玥 郭大静[1] 

机构地区：[1]重庆医科大学附属第二医院放射科,重庆

出　　处：《临床医学进展》2024年第4期615-622,共8页Advances in Clinical Medicine

摘　　要：动脉狭窄是动脉粥样硬化疾病的主要结局之一,其进展和稳定性对动脉粥样硬化疾病的预后和治疗产生重大影响。现有的影像学方法对于动脉粥样硬化性狭窄(Atherosclerotic stenosis, AS)的准确评估存在局限性,而人工智能(Artificial intelligence, AI)在医学影像分析中发挥重要作用,可以实现对病变严重程度和进展速度的定量评估及风险预测。目前,基于AI的医学影像学在动脉狭窄定量评估方面取得了显著进展,尤其是基于深度学习(Deep learning, DL)的算法在血管狭窄预测、斑块分类和识别中表现出良好的性能。本文对基于AI的医学影像学检查在AS定量评估中的研究进展进行综述,并对未来基于AI技术在动脉狭窄定量评估中可能存在的挑战和机遇进行展望。Arterial stenosis is one of the main outcomes of atherosclerotic diseases, and its progression and stability have a significant impact on the prognosis and treatment of atherosclerotic diseases. Existing imaging methods have limitations for accurate assessment of atherosclerotic stenosis (AS), and artificial intelligence (AI) plays an important role in medical image analysis. The quantitative evaluation and risk prediction of the severity and progression of the disease can be realized. At present, AI-based medical imaging has made remarkable progress in the quantitative assessment of arterial stenosis, especially the algorithm based on deep learning (DL) has shown good performance in the prediction of arterial stenosis, plaque classification and recognition. This article reviews the research progress of AI-based medical imaging in quantitative assessment of AS, and looks forward to the possible challenges and opportunities of AI-based quantitative assessment of arterial stenosis in the future.

关 键 词：动脉粥样硬化性狭窄 人工智能 医学影像学 定量评估 

分 类 号：R54[医药卫生—心血管疾病]