About: Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia
Creator	Zhou, Min Zhang, Huan Xu, Xin Wang, Lan Chen, Yong Pan, Zilai Qu, Jieming Yao, Weiwu Feng, Jianxing Guan, Mingxin Hospital, Shanghai Huang, Jingwen Jin, Xiaoyan Tan, Jingwen Tong, Shanghai Xu, Yanping Wei, Jingqi Xia, Yihan Yang, Dexiang Zou, Longkuan
source	MedRxiv
abstract	Background: Chest CT had high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at early stage, giving it an advantage over nucleic acid detection in time of crisis. Deep learning was reported to discover intricate structures from clinical images and achieve expert-level performance in medical image analysis. To develop and validate an integrated deep learning framework on chest CT images for auto-detection of NCP, particularly focusing on differentiating NCP from influenza pneumonia (IP). Methods: 35 confirmed NCP cases were consecutively enrolled as training set from 1138 suspected patients in three NCP designated hospitals together with 361 confirmed viral pneumonia patients from center one including 156 IP patients, from May, 2015 to February, 2020. The external validation set enrolled 57 NCP patients and 50 IP patients from eight centers. Results: 96.6% of NCP lesions were larger than 1 cm and 76.8% were with intensity below -500 Hu, indicating less consolidation than IP lesions which had nodules ranging 5-10 mm. The classification schemes accurately distinguished NCP and IP lesions with area under the receiver operating characteristic curve (AUC) above 0.93. The Trinary scheme was more device-independent and consistent with specialists than the Plain scheme, which achieved a F1 score of 0.847, higher than the Plain scheme (0.774), specialists (0.785) and residents (0.644). Conclusions: Our study potentially provides an accurate early diagnosis tool on chest CT for NCP with high transferability, and shows high efficiency in differentiating NCP and IP, helping to reduce misdiagnosis and contain the pandemic transmission.
has issue date	2020-03-30(xsd:dateTime)
bibo:doi	10.1101/2020.03.24.20043117
has license	medrxiv
sha1sum (hex)	dd6fdf2cc09a1ccaf015c0c74902bc2d3b297d4f
schema:url	https://doi.org/10.1101/2020.03.24.20043117
resource representing a document's title	Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia
resource representing a document's body	covid:dd6fdf2cc09a1ccaf015c0c74902bc2d3b297d4f#body_text
is schema:about of	named entity 'pneumonia' named entity 'detection' named entity 'early' named entity 'CRISIS' covid:arg/dd6fdf2cc09a1ccaf015c0c74902bc2d3b297d4f named entity 'medical image analysis' named entity 'Deep learning' named entity 'NCP' named entity 'chest CT' named entity 'pneumonia' named entity 'pneumonia' named entity 'coronavirus' named entity 'pneumonia' named entity 'influenza' named entity 'influenza' named entity 'preprint' named entity 'validation set' named entity 'SARS-CoV-2' named entity 'chest CT scan' named entity 'deep learning' named entity 'misdiagnosis' named entity 'AlexNet' named entity 'NCP' named entity 'medRxiv' named entity 'nucleic acid test' named entity 'chest CT' named entity 'lesion' named entity 'preprint' named entity '16,000' named entity 'validation set' named entity 'NCP' named entity 'NCP' named entity 'chest CT' named entity 'epithelial cells' named entity 'GGO' named entity 'viral pneumonia' named entity 'NCP' named entity 'retrospective study' named entity 'NCP' named entity 'necrotizing' named entity 'usual interstitial pneumonia' named entity 'validation set' named entity 'medRxiv' named entity 'lung lobes' named entity 'ACE2' named entity 'infection' named entity 'deep learning' named entity 'CT image' named entity 'deep learning' named entity 'preprint' named entity 'viral pneumonia' named entity 'lesion' named entity 'influenza' named entity 'NCP' named entity 'NCP' named entity 'sensitivity, specificity' named entity 'routes of transmission' named entity 'differential diagnosis' named entity 'lower respiratory tract' named entity 'infection' named entity 'radiologists' named entity 'validation set' named entity 'preprint' named entity 'nucleic acid detection' named entity 'United States' named entity 'pathogens'

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