Diagnosis of COVID-19 associated pneumonia based on density distribution features and machine learning(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第3期
- Page:
- 387-391
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Diagnosis of COVID-19 associated pneumonia based on density distribution features and machine learning
- 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
- Keywords:
- Keywords: novel corona virus pneumonia density distribution features machine learning
- PACS:
- R318；R563.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.03.022
- 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.
Last Update: 2021-03-30