ABSTRACT
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. It is a noninvasive imaging approach that can depict certain characteristic manifestations in the lung associated with COVID-19. Therefore, CT could serve as an effective way for early screening and diagnosis of COVID-19. Despite its advantages, CT may demonstrate similar imaging features between COVID-19 and other types of pneumonia, thus making it difficult to differentiate.
Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Recently, artificial intelligence using deep learning technology has demonstrated great success in the medical imaging domain due to its high capability of feature extraction. Specifically, deep learning was applied to detect and differentiate bacterial and viral pneumonia on pediatric chest radiographs. Attempts have also been made to detect various imaging features of chest CT. The purpose of this chapter is to apply different deep learning frameworks to detect COVID-19 infection using chest CT. The main characteristics of the different techniques are presented, and their performance is analyzed. Scans of community-acquired pneumonia (CAP) and other non-pneumonia lung disease were included to test the robustness of the model. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.
