Article ; Online: Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.
2020 Volume 181, Issue 6, Page(s) 1423–1433.e11
Abstract: ... our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider ... it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing ... appropriate early clinical management and allocate resources appropriately. We have made this AI system ...
| Abstract | Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19. |
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| MeSH term(s) | Artificial Intelligence ; COVID-19 ; China ; Cohort Studies ; Coronavirus Infections/diagnosis ; Coronavirus Infections/pathology ; Coronavirus Infections/therapy ; Datasets as Topic ; Humans ; Lung/pathology ; Models, Biological ; Pandemics ; Pilot Projects ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/pathology ; Pneumonia, Viral/therapy ; Prognosis ; Radiologists ; Respiratory Insufficiency/diagnosis ; Tomography, X-Ray Computed |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-05-04 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 187009-9 |
| ISSN | 1097-4172 ; 0092-8674 |
| ISSN (online) | 1097-4172 |
| ISSN | 0092-8674 |
| DOI | 10.1016/j.cell.2020.04.045 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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