北京生物医学工程2025，Vol.44Issue(3)：315-321,7.DOI:10.3969/j.issn.1002-3208.2025.03.015
基于超声心动图的心脏瓣膜反流严重程度智能评估方法研究进展
Research progress on intelligent assessment of cardiac valve regurgitation severity based on echocardiography
摘要
Abstract
Mitral valve regurgitation is a significant factor affecting cardiac ejection function and stands as a crucial contributor to the onset and mortality of cardiovascular diseases.Currently,echocardiography is a widely employed and effective diagnostic method during the evaluation and treatment of valvular regurgitation.Serving as a low-cost,efficient,and safe technology,ultrasound plays a pivotal role in the assessment of the severity of valvular regurgitation.However,ultrasound diagnostic techniques can be susceptible to human factors,and the reliability of its diagnostic outcomes needs further enhancement.Nevertheless,with the advancement and application of artificial intelligence technology,AI-based assessment methods have demonstrated the capability to rapidly analyze multiple parameters within echocardiographic images,such as regurgitant orifice area,flow velocity,and regurgitant time.This advancement enables accurate and intelligent assessment of the severity of regurgitation.In contrast to traditional manual interpretation,intelligent assessment provides faster and more reliable diagnostic results,further improving the precision and reliability of evaluating valvular regurgitation.This,in turn,assists medical professionals in making more accurate judgments and enhances the diagnostic and treatment capabilities for valvular regurgitation.The integration of ultrasound with artificial intelligence is gradually revealing its potential advantages and has become a critical technological tool in the field of assessing the severity of valvular regurgitation.This article will introduce state-of-the-art methods for the intelligent assessment of valvular regurgitation severity based on echocardiography,including unsupervised machine learning techniques that utilize feature extraction from echocardiographic data and supervised deep learning methods such as convolutional neural networks and self-supervised learning.It aims to provide an overview of the current research progress in ultrasound-based intelligent assessment of valvular regurgitation severity.关键词
心脏瓣膜反流/超声心动图/严重程度/智能评估方法Key words
heart valve regurgitation/echocardiography/severity/intelligent assessment method分类
基础医学引用本文复制引用
刘韩,舒庆兰,王胰,彭博,尹立雪,谢盛华..基于超声心动图的心脏瓣膜反流严重程度智能评估方法研究进展[J].北京生物医学工程,2025,44(3):315-321,7.基金项目
四川省区域创新合作项目(2023YF00006)、四川省自然科学基金(2022NSFSC0662)资助 （2023YF00006）
