基于血管内超声的机器学习在冠状动脉病变中的研究进展

Advances in the application of machine learning based on intravascular ultrasound in coronary artery disease

血管内超声图像是心血管疾病临床诊疗的重要参考数据。医生对血管内超声图像信息的判断在冠状动脉病变的诊断和治疗方面具有重要作用。机器学习能够提取出人肉眼无法感知的图像信息,进行数据分析并构建医学诊断模型,有助于判定斑块的稳定性,预测疾病进程以及患者的临床结局,在辅助临床工作方面有一定作用。笔者就机器学习方法在冠状动脉血管内超声图像的应用进展进行综述,并探讨其局限性及发展方向。

Intravascular ultrasound(IVUS)is an essential source of information for the clinical diagnosis and management of coronary artery disease.The diagnosis and management of coronary artery disease heavily relies on the medical professionals′interpretation of IVUS images.Machine learning can analyze data,create medical diagnostic models,and extract information from IVUS images that human eyes cannot perceive.These capabilities help enhance the diagnosis of coronary artery disease,forecast patients′disease states and clinical outcomes,and play a significant role in supporting clinical work.This article discusses the limitations and potential applications of machine learning techniques in IVUS for coronary artery imaging.