Chapter title |
A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.
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Chapter number | 30 |
Book title |
Multiplex Biomarker Techniques
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Published in |
Methods in molecular biology, January 2022
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DOI | 10.1007/978-1-0716-2395-4_30 |
Pubmed ID | |
Book ISBNs |
978-1-07-162394-7, 978-1-07-162395-4
|
Authors |
Sahebkar, Amirhossein, Abbasifard, Mitra, Chaibakhsh, Samira, Guest, Paul C, Pourhoseingholi, Mohamad Amin, Vahedian-Azimi, Amir, Kesharwani, Prashant, Jamialahmadi, Tannaz, Guest, Paul C., Amirhossein Sahebkar, Mitra Abbasifard, Samira Chaibakhsh, Paul C. Guest, Mohamad Amin Pourhoseingholi, Amir Vahedian-Azimi, Prashant Kesharwani, Tannaz Jamialahmadi |
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. |
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Lecturer | 1 | 13% |
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