Journal
EUROPEAN RESPIRATORY JOURNAL
Volume 56, Issue 2, Pages -Publisher
EUROPEAN RESPIRATORY SOC JOURNALS LTD
DOI: 10.1183/13993003.00775-2020
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [81930053, 81227901, 81871332, 61936013, 81771806]
- National Key R&D Program of China [2017YFA0205200]
- Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province [2020FCA015]
- Fundamental Research Funds for the Central Universities [2042020kfxg10]
- Anhui Natural Science Foundation [202004a07020003]
- Hubei Health Committee General Program [WJ2019M043]
- Anti-schistosomiasis Fund during 2019-2020 [WJ2019M043]
- Beijing Municipal Commission of Health [2020-TG-002]
- Youan Medical Development Fund [BJYAYY-2020YC-03]
- China Postdoctoral Science Special Foundation [2019TQ0019]
- Crossref Funder Registry
Ask authors/readers for more resources
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 >5 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available