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A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Shuo Wang1,2, Yunfei Zha3,2, Weimin Li4,2
1Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
Insights
A deep learning system can rapidly diagnose COVID-19 from CT scans and identify high-risk patients. This AI tool aids in optimizing medical resources and early prevention strategies for better patient outcomes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Infectious Diseases
Background:
- Global spread of Coronavirus disease 2019 (COVID-19) has strained medical resources.
- Efficient diagnosis and risk stratification are crucial for patient management and resource optimization.
- Computed tomography (CT) is a key imaging modality for respiratory illness assessment.
Purpose of the Study:
- To develop and validate a fully automatic deep learning system for COVID-19 diagnosis using CT.
- To assess the system's ability to predict patient prognosis and stratify risk.
- To evaluate the system's utility in clinical settings for rapid screening and resource allocation.
Main Methods:
- Retrospective collection of 5372 CT images from patients across seven cities/provinces.
- A deep learning system was pre-trained on 4106 images to learn lung features.
- The system was trained and externally validated on 1266 patients (924 COVID-19, 342 other pneumonia).
Main Results:
- The deep learning system demonstrated strong performance in identifying COVID-19 from other pneumonia (AUCs 0.87-0.88) and viral pneumonia (AUC 0.86).
- The system successfully stratified patients into high- and low-risk groups with significant differences in hospital stay duration (p=0.013, p=0.014).
- The automated system focused on abnormal lung areas consistent with radiological findings without human intervention.
Conclusions:
- Deep learning offers a valuable tool for rapid COVID-19 screening via CT scans.
- The system aids in identifying potentially high-risk patients, facilitating early intervention.
- This technology can support medical resource optimization and proactive patient management.
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 >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.
