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心脑血管疾病是全球主要的致死和致残病因之一。尽管医疗技术不断进步，心脑血管疾病的诊治仍存在许多亟待解决的问题，人工智能与机器学习的出现为应对这些问题提供了新途径。美国心脏学会2024年发布的科学声明从影像分析、心电图判读、床旁监护、可穿戴设备、基因组学及电子健康记录等方面介绍了人工智能与机器学习在临床中的应用及面临的挑战。本文对该指南进行介绍，并分析人工智能与机器学习在脑血管病领域中应用的优势，为我国未来在这一领域的研究提供参考。 Abstract: Cardiovascular and cerebrovascular diseases are among the leading causes of death and disability worldwide. Despite continuous advancements in medical technology, the diagnosis and treatment of these diseases still face many unresolved challenges. The emergence of artificial intelligence and machine learning provides new approaches to addressing these issues. The American Heart Association’s 2024 scientific statement highlights the applications and challenges of artificial intelligence and machine learning in clinical scenarios, including imaging analysis, electrocardiogram interpretation, bedside monitoring, wearable devices, genomics, and electronic health records. This paper aims to introduce these aspects and analyze the advantages of artificial intelligence and machine learning in the field of cerebrovascular diseases, providing a reference for the future development of artificial intelligence and machine learning in this area in China.

未破裂颅内动脉瘤形态学分析被广泛用于动脉瘤破裂的风险评估和术前规划。三维视角较既往的二维视角更能精确体现动脉瘤的形态变化，但人工测量在三维视角下准确度较低且可重复性差，而人工智能在三维视角下的测量更有优势。但是，目前尚缺乏人工智能在三维视角下的动脉瘤形态测量方法。考虑到三维视角下的规范化测量对形态学参数的临床和科研运用具有重要意义，本文提出了一套三维视角下基于人工智能的未破裂颅内动脉瘤形态学指标测量方法，以期推动动脉瘤研究的同质化和临床诊疗的规范化发展。 Abstract: Morphological analysis of unruptured intracranial aneurysms is widely used in risk assessment and surgical design. Compared with the manual measurement in the two-dimensional perspective, the three-dimensional perspective can reflect the morphology changes of aneurysms more accurately. But the manual measurement in the three-dimensional perspective has poor accuracy and repeatability. Therefore, the measurement of artificial intelligence in the three-dimensional perspective has greater advantages than that of manual measurement. However, the method of aneurysm morphological measurement in the three-dimensional perspective based on artificial intelligence has not been proposed. The standardized measurement in the three-dimensional perspective is of great significance to the clinical and research application of morphological parameters. In this paper, a three-dimensional measurement method for unruptured intracranial aneurysms based on artificial intelligence is presented, in order to promote the homogenization of aneurysm research and the standardization of clinical diagnosis and treatment.