[1]韩冬,于勇,贺太平,等.基于密度分布特征及机器学习诊断COVID-19相关性肺炎[J].中国医学物理学杂志,2021,38(3):387-391.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.022]
　HAN Dong,YU Yong,HE Taiping,et al.Diagnosis of COVID-19 associated pneumonia based on density distribution features and machine learning[J].Chinese Journal of Medical Physics,2021,38(3):387-391.[doi:DOI:10.3969/j.issn.1005-202X.2021.03.022]
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基于密度分布特征及机器学习诊断COVID-19相关性肺炎()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第3期
页码:
387-391
栏目:
医学人工智能
出版日期:
2021-03-30

文章信息/Info

Title:
Diagnosis of COVID-19 associated pneumonia based on density distribution features and machine learning
文章编号:
1005-202X（2021）03-0387-05
作者:
韩冬1; 于勇2; 贺太平2; 段海峰1; 贾永军1; 张喜荣2; 郭佑民3; 于楠1
1.陕西中医药大学附属医院医学影像科， 陕西 咸阳 712000；2.陕西中医药大学医学技术学院， 陕西 咸阳 712000； 3.西安交通大学第一附属医院医学影像科， 陕西 西安 710061
Author(s):
HAN Dong1;  YU Yong2;  HE Taiping2;  DUAN Haifeng1;  JIA Yongjun1;  ZHANG Xirong2;  GUO Youmin3;  YU Nan1
1. Department of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, China 2. School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang 712000, China 3. Department of Medical Imaging, the First Affiliated Hospital of Xian Jiaotong University, Xian 710061, China
关键词:
新型冠状病毒; 肺炎; 密度分布特征; 机器学习
Keywords:
Keywords: novel corona virus pneumonia density distribution features machine learning
分类号:
R318；R563.1
DOI:
DOI:10.3969/j.issn.1005-202X.2021.03.022
文献标志码:
A
摘要:
目的：基于密度分布特征及机器学习诊断新型冠状病毒（COVID-19）相关性肺炎。方法：回顾性收集经荧光逆转录聚合酶链反应检测确诊COVID-19的患者42例（COVID-19组），社区获得性肺炎43例（对照组）。共获得211份胸部CT图像，以6：4比例分层抽样为训练集（126份）及验证集（85份）。采用一种CAD软件中的肺炎模块获得肺炎不同密度区间所占全肺体积的百分比（P/L%）。密度分布特征降维后采用支持向量机（SVM）建模，并评价4种核函数的SVM模型的诊断效能。结果：两组患者的年龄、性别及出现胸膜腔积液的构成比差异均无统计学意义（P>0.05）。肺炎密度分布特征降维后获得32个特征。基于该32个特征建立的4种核函数SVM模型中，多项式SVM模型在验证集的效能最高，受试者特征曲线（ROC）的曲线下面积为0.897（95%可信区间0.828~0.966），P<0.001。准确性为0.906（95%可信区间0.823~0.959），敏感性为0.906，特异性为0.906。结论：基于密度分布特征及机器学习诊断COVID-19相关性肺炎有较高的效能，有助于快速筛选COVID-19患者。
Abstract:
Abstract: Objective To diagnose corona virus disease 2019 (COVID-19) associated pneumonia based on density distribution features and machine learning. Methods The clinical information of 42 patients with COVID-19 confirmed by RT-PCR (COVID-19 group) and 43 patients with community-acquired pneumonia (control group) were retrospectively collected. A total of 211 chest CT images were obtained, and according to stratified sampling based on a proportion of 6 to 4, the chest images were divided into training set (126) and validation set (85). The percentages of different density intervals of pneumonia in the total lung volume (P/L%) were obtained using a pneumonia module in CAD software. Support vector machine (SVM) was used for modeling after the dimensionality reduction of density distribution features, and the diagnostic efficiency of SVM models with 4 different kernel functions was evaluated. Results There was no significant difference in age, gender and constituent ratio of pleural effusion between two groups (P>0.05). A total of 32 features were obtained after the dimensionality reduction of pneumonia density distribution features. Among SVM models with 4 different kernel functions based on these 32 features, polynomial SVM model has the highest efficiency in validation set, and the area under receiver operating characteristic curve was 0.897 (95% confidence interval 0.828-0.966) (P<0.001). The accuracy, sensitivity and specificity of polynomial SVM model were 0.906 (95% confidence interval: 0.823-0.959), 0.906 and 0.906. Conclusion The diagnosis of COVID-19 associated pneumonia based on the density distribution features and machine learning has a high efficiency, which is helpful for the rapid screening of COVID-19 patients.

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备注/Memo

备注/Memo:
【收稿日期】2020-10-16 【基金项目】陕西中医药大学学科创新团队建设项目(2019-QN092019-YS04) 【作者简介】韩冬，主治医师，研究方向：机器学习在医学影像的临床应用，E-mail: hundnn@qq.com 【通信作者】于楠，副教授，研究方向：胸部影像学，E-mail: yunan0512@sina.com
更新日期/Last Update: 2021-03-30