摘要
颈动脉斑块的识别和成分诊断,对于斑块稳定性的评估、引发脑卒中等事件的风险预测和治疗方案的选择等具有重要意义。机器学习算法通过自动识别影像学颈动脉斑块以及判断斑块内主要成分,可以缓解医生的视觉负担、降低工作量、减轻医疗压力,具有重要的潜在应用价值。文中综述机器学习算法在影像学颈动脉斑块分类中的研究进展,就机器学习算法在颈动脉斑块分类中的研究流程、算法和文献进行总结和归纳。
The identification and component diagnosis of carotid plaques are of great significance for the assessment of plaque stability,the risk prediction of stroke-related events,and the choice of treatment options.The "machine learning"algorithms can effectively alleviate the visual burden of doctors,reduce the work of doctors,and relieve medical pressure by automatically identifying carotid plaques and diagnosing the main components in the plaque,which have important potential application value.The research progress of machine learning algorithms and literature of machine learning in carotid plaque classification were reviewe.
作者
吴秋雯
李郁欣
黄磊
周书怡
胡斌
鲍奕仿
耿辰
夏威
杨丽琴
耿道颖
WU Qiu-wen;LI Yu-xin;HUANG Lei;ZHOU Shu-yi;HU Bin;BAO Yi-fang;GENG Chen;XIA Wei;YANG Li-qin;GENG Dao-ying(2.Department of Radiology,Huashan Hospital,Fudan University,Shanghai 200040,China;Institute of Functional and Molecular Medical Imaging,Fudan University,Shanghai 200040,China)
出处
《中国临床神经科学》
2019年第4期458-465,共8页
Chinese Journal of Clinical Neurosciences
基金
上海市科技创新行动计划临床医学领域项目(编号:17411953700)
复旦大学附属华山医院科研启动基金(编号:2016QD085)
关键词
颈动脉斑块
机器学习
超声
磁共振成像
carotid plaque
machine learning
ultrasound
magnetic resonance imaging