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In Methods in molecular biology (Clifton, N.J.)

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.

Sahebkar Amirhossein, Abbasifard Mitra, Chaibakhsh Samira, Guest Paul C, Pourhoseingholi Mohamad Amin, Vahedian-Azimi Amir, Kesharwani Prashant, Jamialahmadi Tannaz


COVID-19, Chest CT, Computed tomography, Deep learning, Diffuse opacities, Lesion distribution, SARS-CoV-2