摘要
如今,冠心病仍然是影响人们生活质量和导致死亡的主要原因,因此冠状动脉病变的检测及治疗至关重要。光学相干断层成像是冠状动脉内斑块组织的鉴别方法。但由于医师的临床经验差异性,导致在诊断中产生分歧使患者无法得到精准治疗。深度学习作为最先进的算法,能够自动提取深层特征,提高其在影像学诊断中的准确性和可靠性。本文将以深度学习在光学相干断层成像的应用进行综述,并对其在冠心病诊断中的未来前景进行展望。
The high mortality rate of coronary artery disease affects the quality of life today,so the detection and treatment of coronary artery lesions is crucial.Intravascular optical coherence tomography is the best method to identify plaque tissue within coronary arteries.However,the varying levels of clinical experience of physicians lead to disagreement in diagnosis preventing patients from receiving accurate treatment.Deep learning,as the most advanced technology,can automatically extract deep features,which can improve its accuracy and reliability in imaging diagnosis.In this paper,we review the application of deep learning in intravascular optical coherence tomography and provide an outlook on its future prospects in the diagnosis of coronary artery disease.
作者
哈力木拉提·买买提
艾克力亚尔·艾尼瓦尔
凯赛尔江·卡地尔
刘鹏飞
秦练
马翔
Halimulati Maimaiti;Aikeliyaer Ainiwaer;Kaisaierjiang Kadier;LIU Peng-fei;QIN Lian;MA Xiang(Department of Cardiology,the First Affi liated Hospital of Xinjiang Medical University,Urumqi 830054,China)
出处
《中国介入心脏病学杂志》
CSCD
2024年第3期154-158,共5页
Chinese Journal of Interventional Cardiology
基金
新疆维吾尔自治区重点研发任务专项项目(2022B03022-3)。
关键词
深度学习
冠心病
血管内光学相干断层成像
Deep learning
Coronary heart disease
Optical coherence tomography
