| Literature DB >> 35838977 |
Amirhossein Sahebkar1,2,3,4,5, Mitra Abbasifard6,7, Samira Chaibakhsh8, Paul C Guest9, Mohamad Amin Pourhoseingholi10, Amir Vahedian-Azimi11, Prashant Kesharwani12, Tannaz Jamialahmadi13.
Abstract
There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.Entities:
Keywords: COVID-19; Chest CT; Computed tomography; Deep learning; Diffuse opacities; Lesion distribution; SARS-CoV-2
Mesh：
Year: 2022 PMID： 35838977 DOI： 10.1007/978-1-0716-2395-4_30
Source DB: PubMed Journal: Methods Mol Biol ISSN： 1064-3745