About: A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

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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
title	A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis
Creator	Li, Hongjun Li, Xiaohu Gong, Wei Li, Li Bai, Yan Wang, Meiyun Wang, Shuo Niu, Meng Li, Weimin Tian, Jie Zha, Yunfei Qiu, Xiaoming Wang, Liusu Wu, Qingxia Yu, He Zhu, Yongbei
source	Medline; PMC
abstract	Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography. We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system. In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings. Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.
has issue date	2020-05-22(xsd:dateTime)
bibo:doi	10.1183/13993003.00775-2020
bibo:pmid	32444412
has license	cc-by-nc
sha1sum (hex)	cc65850db18e6a849fe5f1a3a7c97e1b64e9d241
schema:url	https://doi.org/10.1183/13993003.00775-2020
resource representing a document's title	A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis
has PubMed Central identifier	PMC7243395
has PubMed identifier	32444412
schema:publication	Eur Respir J
resource representing a document's body	covid:cc65850db18e6a849fe5f1a3a7c97e1b64e9d241#body_text
is schema:about of	named entity 'COVID-19' covid:arg/cc65850db18e6a849fe5f1a3a7c97e1b64e9d241 named entity 'patients' named entity 'COVID-19' named entity 'high-risk' named entity 'Fully Automatic' named entity 'Deep Learning' named entity 'pneumonia' named entity 'COVID' named entity 'global health emergency' named entity 'clinical outcomes' named entity 'prognosis' named entity 'Clinical characteristics' named entity 'CT image' named entity 'convolution' named entity 'high-risk' named entity 'COVID-19' named entity 'coronavirus disease 2019' named entity 'transfer learning' named entity 'lung cancer' named entity 'pneumonia' named entity 'Kaplan-Meier' named entity 'deep learning' named entity 'prognosis' named entity 'validation set' named entity 'probability' named entity 'inference process' named entity 'convolution' named entity 'COVID-19' named entity 'algorithm' named entity 'neuron' named entity 'COVID' named entity 'lesion' named entity 'lung disease' named entity 'COVID' named entity 'vector' named entity 'clinical outcome' named entity 'mutation' named entity 'CT image' named entity 'decision tree' named entity 'chest CT' named entity 'radiologists' named entity 'CT image' named entity 'validation set' named entity 'inference' named entity 'COVID' named entity 'chest CT' named entity 'receiver operating characteristic' named entity 'inference' named entity 'ImageNet' named entity 'COVID' named entity 'COVID' named entity 'set 4' named entity 'GGO' named entity 'diagnostic tool' named entity 'chest CT' named entity 'RT-PCR' named entity 'COVID' named entity 'COVID-19' named entity 'epidermal growth factor receptor' named entity 'epidemic' named entity 'EGFR' named entity 'gene' named entity 'chest CT' named entity 'EGFR' named entity 'inference process'

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