About: A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

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About: A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images
Creator	Yang, Yi Wang, Li Chen, Wen Yang, Liu Zhang, Long Fang, Yi Kong, Hong Lam, Pok Guang, & Lu, Ming Ni, Qianqian Qi, Li Zhang, Xinyuan Zhou, Zhen Yu, Yizhou Xing, Zijian Yuan, Zhi
Source	Medline; PMC
abstract	OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists’ reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm(3). An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-07044-9) contains supplementary material, which is available to authorized users.
has issue date	2020-07-02(xsd:dateTime)
bibo:doi	10.1007/s00330-020-07044-9
bibo:pmid	32617690
has license	no-cc
sha1sum (hex)	d0beaa03e0fc2bc9b83ae1260090dd196df54a11
schema:url	https://doi.org/10.1007/s00330-020-07044-9
resource representing a document's title	A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images
has PubMed Central identifier	PMC7331494
has PubMed identifier	32617690
schema:publication	Eur Radiol
resource representing a document's body	covid:d0beaa03e0fc2bc9b83ae1260090dd196df54a11#body_text
is schema:about of	named entity 'radiological' named entity 'confidence interval' named entity 'pathogen' named entity 'F1 score' named entity 'demonstrated' named entity 'algorithm' named entity 'Objectives' named entity 'algorithm' named entity 'Results' named entity 'LUNG LOBE' named entity 'OBJECTIVES' named entity 'SENSITIVITY' named entity 'PROCESSING' named entity 'REFERENCE STANDARD' named entity 'DEFINITE' named entity 'MEDIAN' named entity '0.94' named entity 'ITS' named entity 'ASSESSING' named entity 'TIME' named entity 'ACCURACY' named entity 'ACROSS' named entity 'assessing' named entity 'residents' named entity 'excellent' named entity 'algorithm' named entity 'model' named entity 'algorithm' named entity 'deep learning' named entity 'compared' named entity 'assessing' named entity 'patients' named entity 'speed' named entity 'detecting' named entity 'images' named entity 'basis' named entity 'performance' named entity 'lesions' named entity 'deep learning' named entity 'chest' named entity 'patients' named entity 'automatic' named entity 'standard' named entity 'outbreak' named entity 'calculated' named entity 'algorithm' named entity 'deep learning' named entity '95% CI' named entity 'chest CT' named entity 'COVID-19' named entity 'deep learning' named entity 'pneumonia' named entity 'algorithm' named entity 'lesion' named entity 'COVID-19' named entity 'pneumonia' named entity 'algorithm' named entity 'radiologists' named entity 'algorithm' named entity 'chest CT' named entity 'lesion' named entity 'lesion' named entity 'lesion' named entity 'chest CT' named entity '0.98'

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