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Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?

文献类型：期刊论文

作者Hu, Shasha5; Zhu, Yongbei4; Dong, Di3,4; Wang, Bei5; Zhou, Zuofu1; Wang, Chi5; Tian, Jie2,4; Peng, Yun5
刊名JOURNAL OF DIGITAL IMAGING
出版日期2022-05-18
页码12
关键词Chest radiographs Convolution neural network Community-acquired pneumonia Pediatric Etiology
ISSN号0897-1889
DOI10.1007/s10278-021-00543-1
通讯作者Tian, Jie(tian@ieee.org) ; Peng, Yun(ppengyun@yahoo.com)
英文摘要Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study included 1769 cases of pediatric pneumonia (viral pneumonia, n = 487; bacterial pneumonia, n = 496; and mycoplasma pneumonia, n = 786). The chest radiographs of the first examination, C-reactive protein (CRP), and white blood cell (WBC) were collected for analysis. All patients were stochastically divided into training, validation, and test cohorts in a 7:1:2 ratio. Automatic lung segmentation and hand-crafted pneumonia lesion segmentation were performed, from which three image-based models including a full-lung model, a local-lesion model, and a context-fusion model were built; two clinical characteristics were used to build a clinical model, while a logistic regression model combined the best CNN model and two clinical characteristics. Our experiments showed that the context-fusion model which integrated the features of the full-lung and local-lesion had better performance than the full-lung model and local-lesion model. The context-fusion model had area under curves of 0.86, 0.88, and 0.93 in identifying viral, bacterial, and mycoplasma pneumonia on the test cohort respectively. The addition of clinical characteristics to the context-fusion model obtained slight improvement. Mycoplasma pneumonia was more easily identified compared with the other two types. Using chest radiographs, we developed a context-fusion CNN model with good performance for noninvasively diagnosing the etiology of community-acquired pneumonia in children, which would help improve early diagnosis and treatment.
WOS关键词INFLAMMATORY MARKERS ; CLINICAL-FEATURES ; DIAGNOSIS ; UTILITY
资助项目Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority[XTCX201814] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000797262700001
出版者SPRINGER
资助机构Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority ; National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/49461]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Peng, Yun
作者单位1.Fujian Med Univ, Fujian Prov Matern & Childrens Hosp, Dept Radiol, Fuzhou 350000, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Beijing Key Lab Mol Imaging Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Children Hlth, Dept Radiol, Beijing 100045, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Shasha,Zhu, Yongbei,Dong, Di,et al. Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?[J]. JOURNAL OF DIGITAL IMAGING,2022:12.
APA Hu, Shasha.,Zhu, Yongbei.,Dong, Di.,Wang, Bei.,Zhou, Zuofu.,...&Peng, Yun.(2022).Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?.JOURNAL OF DIGITAL IMAGING,12.
MLA Hu, Shasha,et al."Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?".JOURNAL OF DIGITAL IMAGING (2022):12.

入库方式： OAI收割

来源：自动化研究所

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