About: A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

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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 System to Screen Novel Coronavirus Disease 2019 Pneumonia
Creator	Zhao, Hong Ruan, Lingxiang Li, Yongtao Liu, Jun Wu, Wei Li, Lanjuan Sheng, Jifang Jiang, X Xu, Xiaowei Yu, Liang Xu, Kaijin Liang, Tingbo Ni, Qin Qiu, Yunqing Li, Y Du, Peng Chen, Y Li, L Li, X Liu, J Ma, C Qiu, Y Zhao, H Liang, T Xu, X Su, J Chen, Yanfei Jiang, Xiangao Lang, Guanjing Li, Xukun Lv, Shuangzhi Ma, Chunlian Su, Junwei Wu, W Xu, K Du, P Yu, L Lang, G Lv, S Ni, Q Ruan, L Sheng, J
Source	Elsevier; Medline; PMC
abstract	The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as Influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-or Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
has issue date	2020-06-27(xsd:dateTime)
bibo:doi	10.1016/j.eng.2020.04.010
bibo:pmid	32837749
has license	no-cc
sha1sum (hex)	444d5b6a96a7daf324df3bf0cbe17743df432fb6
schema:url	https://doi.org/10.1016/j.eng.2020.04.010
resource representing a document's title	A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
has PubMed Central identifier	PMC7320702
has PubMed identifier	32837749
schema:publication	Engineering (Beijing)
resource representing a document's body	covid:444d5b6a96a7daf324df3bf0cbe17743df432fb6#body_text
is schema:about of	named entity 'confidence' named entity 'patients' named entity 'age' named entity 'patients' named entity 'patients' named entity 'CT image' named entity 'imaging' named entity 'These' named entity 'Novel' named entity 'TAKEN' named entity 'CT IMAGE' named entity 'INDIVIDUAL' named entity 'COMPUTED TOMOGRAPHY' named entity 'BENCHMARK DATASET' named entity 'MANIFESTATIONS' named entity 'GROUPS' named entity 'manifestations' named entity 'models' named entity 'computed tomography' named entity 'images' named entity 'male' named entity 'doctors' named entity 'viral' named entity 'samples' named entity 'detection' named entity 'hospitals' named entity 'deep learning' named entity 'COVID' named entity 'infection' named entity 'diagnostic method' named entity 'infection' named entity 'Bayesian' named entity 'reverse transcription-polymerase chain reaction' named entity 'digital image processing' named entity 'COVID-19' named entity 'COVID' named entity 'COVID-19' named entity 'algorithm' named entity 'Step 2' named entity 'research team'

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