摘要
高血压脑出血(hypertensive intracerebral hemorrhage,HICH)是一种具有突发性、快速进展、高致死率和致残率的疾病。非对比剂CT(non-contrast computer tomography,NCCT)是该疾病常规的检查成像方法。目前,通过利用影像组学技术从医学图像中提取高通量特征信息,并结合机器学习(machine learning,ML)算法,可以实现对HICH的快速准确诊断,并对病情进行评估和预测,文中介绍了NCCT影像组学与ML技术在HICH诊断与治疗中应用相关进展及未来相关应用研究的可能方向。
Hypertensive intracerebral hemorrhage(HICH)is a disease with a rapid onset,rapid progression,high mortality rate,and long-term impact on the ability to function.Non-contrast agent-based CT(NCCT)is a common method for evaluating and identifying HICH.Recent radiomics in image processing and machine learning(ML)have enabled the extraction of high-dimensional feature information from medical images,which can be used to rapidly and accurately diagnose HICH and predict its course of disease.The paper describes the application of radiomics and ML techniques in HICH diagnosis and treatment,and identifies possible directions for future research.
作者
姜彦文
秦虎
冷昭富
帕孜力亚·艾克拉木
汪永新
JIANG Yanwen;QIN Hu;LENG Zhaofu;PAZILIYA Aikel-amu;WANG Yongxin(Department of Neurosurgery,the First Affiliated Hospital of Xinjiang Medical University,Urumqi 830054,China)
出处
《中国神经精神疾病杂志》
CAS
CSCD
北大核心
2023年第10期609-614,共6页
Chinese Journal of Nervous and Mental Diseases
基金
新疆维吾尔自治区自然科学基金(编号:2022D01D70)。
关键词
高血压脑出血
影像组学
机器学习
影像特征
深度学习
算法
Hypertensive intracerebral hemorrhage
Radiomics
Machine learning
Image features
Deep learning Algorithm
