机器学习算法在颈动脉斑块影像学分类中的研究进展

Research Progress of Machine Learning in Carotid Plaque Classification

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.