Related Experiment Video
Updated: Sep 4, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection
Amirhossein Sahebkar1,2,3,4,5, Mitra Abbasifard6,7, Samira Chaibakhsh8
1Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. amir_saheb2000@yahoo.com.
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.
