AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

Naser Zaeri

doi:10.5772/intechopen.99442

Abstract

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling 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.

Highlights

We provide an extensive review and a deep study on how artificial intelligence (AI) and machine learning (ML) can help the world to deliver efficient responses and combat the COVID-19 pandemic using computerized tomography (CT) scan imaging
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
This work attempted to provide a detailed study on how the AI and ML can help in various domains related to COVID-19, in the area of disease diagnosis using CT imagery

Summary

Introduction

The world is facing one of its most dangerous risks, if not the most one throughout the century. 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]. 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). 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

A view of AI and ML in healthcare

Convolutional neural network

CT diagnosis algorithms

Findings

Discussion

Conclusion