A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
By Shuo Wang, Y. Zha, Wei-min Li, Qingxia Wu, Xiaohu Li, M. Niu, Meiyun Wang, Xiaoming Qiu, Hongjun Li, He Yu and 6 more. European Respiratory Journal, 2020
440 citations recorded by the source corpus.
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. A fully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis https://bit.ly/3bRaxGw
Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/9954ec0a95fc0840f7712d7b0c67b242bb536a9e
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Citations in the corpus
- The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, methodology): “However, in several studies with the larger dataset ( > 1000 patients) [13], [80], the performance drops.”
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then, given the specified states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”