Abstract
Diagnosis of any disease requires careful assessment. Diagnostic procedures might play a pivotal role in making the treatment protocols more powerful/potent efficient, and effective by highlighting the clinical findings of the disease. Along with other diagnostic techniques, a computed tomography (CT) scan has been employed to diagnose coronavirus disease-19 (COVID-19) patients since the outbreak of this disease and therefore promises it as a crucial diagnostic tool. A CT-scan is a specialized medical imaging technique that produces cross-sectional images of specific areas of a targeted object utilizing a combination of multiple X-ray measurements taken from multiple angles. CT scan help diagnose COVID-19 individuals display severe clinical features and advanced forms of the disease. Pulmonary CT images of COVID-19 patients had common diagnostic manifestations such as ground-glass opacities (GGO), consolidation, reticular pattern, and fibrosis. It also includes nodular lesions reversed halo sign. and thickening of the pleura as the less common findings. The receiver operative characteristic (ROC) curve has been successfully applied for determining the accuracy of the CT-scan-based diagnosis of COVID-19. Artificial intelligence (AI) techniques, particularly deep learning, are extensively used for processing and evaluating imaging data, thereby improving the diagnostic performance of radiologists and clinicians. Despite its emergence as an effective method for screening COVID-19 patients, a CT scan is not recommended as a primary tool for diagnosing COVID-19 and must be used with utmost caution as it may cause the transmission of COVID-19 pathogen in the current epidemic. Overall, the current chapter focuses on CT-scan implications in diagnosing the COVID-19 infection and its comparison with the other diagnostic tools.
Keywords: Computed Tomography, Consolidation, Coronavirus, COVID-19, Disease, Ground-Glass Opacity, Imaging, Receiver Operative Characteristic.