A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.
Eur Respir J
; 56(2)2020 08.
Article
em En
| MEDLINE
| ID: mdl-32444412
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 resource 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 seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5â
days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). 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 of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Pneumonia Viral
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Processamento de Imagem Assistida por Computador
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Infecções por Coronavirus
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Aprendizado Profundo
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Pulmão
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Eur Respir J
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China