**1. Introduction**

Today, the world is facing one of its most dangerous risks, if not the most one throughout the century. It is a pandemic that is draining the whole world's resources and threatening the development of human civilization. The COVID-19 pandemic continues to have a devastating effect on the health and well-being of global population, caused by the infection of individuals by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. On the 30th of January 2020, the WHO declared the SARS-CoV-2 outbreak a public health emergency of international concern. On March 11th, WHO characterized COVID-19 as a pandemic. At the time of writing this manuscript (May 29, 2021), the number of infected people has surpassed 169,118,995 confirmed cases and more than 3,519,175 deaths in 223 countries [2]. The World Trade Organization has announced that the world has effectively entered a recession period. The world's economy and many countries' economies are in danger of collapsing. Schools in many countries are closed and students around the world are forced to stay at home [3, 4].

One of the early challenges that emerged at the beginning of the pandemic is the detection of COVID-19 cases. The most important method used for detecting COVID-19 cases is polymerase chain reaction (PCR) testing, that can detect SARSCoV-2 RNA from respiratory specimens [5]. Though PCR testing is the standard, it is a time-consuming, laborious, and complicated manual process that is in short supply [6]. Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. This limitation of human expert-based diagnosis has provided a strong motivation for the use of computer simulation and modeling to improve the speed and accuracy of the detection process [7, 8]. Another related issue is the manual contouring of lung lesions which tends to be a tedious and time-consuming work, and could lead to subsequent assessment discrepancies in case of inconsistent delineation. Thus, a fast auto-contouring tool for COVID-19 infection is needed in the onsite applications for quantitative disease assessment [9].

Since the early days of this catastrophic crisis, there has been an upsurge in the exploration and use of artificial intelligence (AI), computer simulation, and data modeling and analytic tools, in a multitude of areas. AI and machine learning (ML) have demonstrated great performance in various medical fields and have proven their vital role in complicated therapeutic scenes. These systems have shown high level of accuracy in different applications, such as lung disease classification, breast cancer, skin lesion classification, identifying diabetic retinopathy, and Alzheimer [10–12].

Scientists and healthcare professionals have realized the importance of AI and imaging technologies in slowing the spread of COVID-19 at preliminary stages, and containing the virus at later stages. Currently, many AI and computer modeling systems are used in disease diagnosis, examining, identifying, and treating patients. AI-based simulations have also been employed for evaluating disease progression, economic downturn and recovery, contingency planning, demand sensing, supply chain disruptions, workforce planning, as well as for management decision-making on site openings [13]. For example, AI-based simulations were critical in integrating multiple decision-making domains (e.g., COVID-19 disease progression, government interventions, people behavior, demand sensing, supply disruptions etc.) [14].

In this paper, we provide an extensive review and a deep study on how AI and ML can help the world to deliver efficient responses and combat the COVID-19 pandemic using CT scan imaging. More specifically, we will focus on the modern algorithms in CT scan imaging that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease. We provide recent theoretical developments, technological advancements, and practical implementations of AI algorithms and ML techniques that uses CT imaging to suggest possible solutions in investigating diagnosis, severity level, prediction, tracking, treatments and other decision making scenarios related to COVID-19. In this regard, we explore a vast number of important studies that have been performed by various academic and research communities from numerous disciplines during the period of pandemic since the early days of 2020 up to the very recent days (May 2021). Before we further proceed, we note that many of the articles cited are still preprints at the time of writing this manuscript. Given the fast-moving nature of the crisis, we endeavored to be comprehensive of coverage. We understand that the full scientific rigor for many articles should still be assessed by the scientific community through peerreviewed evaluation and other quality control mechanisms. However, the whole story is a striking dilemma and a big challenge to the global scientific communities.

Researchers, physicians, technical-background individuals, and academics are putting all their efforts to come up with solutions and cures to this fatal disease. All of these efforts have emerged during a very short period of time, and a lot are yet to emerge in the coming few months, and possibly years.
