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Role of Artificial Intelligence in COVID-19 Detection.
Gudigar, Anjan; Raghavendra, U; Nayak, Sneha; Ooi, Chui Ping; Chan, Wai Yee; Gangavarapu, Mokshagna Rohit; Dharmik, Chinmay; Samanth, Jyothi; Kadri, Nahrizul Adib; Hasikin, Khairunnisa; Barua, Prabal Datta; Chakraborty, Subrata; Ciaccio, Edward J; Acharya, U Rajendra.
  • Gudigar A; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
  • Raghavendra U; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
  • Nayak S; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
  • Ooi CP; School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore.
  • Chan WY; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia.
  • Gangavarapu MR; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
  • Dharmik C; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
  • Samanth J; Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India.
  • Kadri NA; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
  • Hasikin K; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
  • Barua PD; Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia.
  • Chakraborty S; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
  • Ciaccio EJ; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
  • Acharya UR; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Sensors (Basel) ; 21(23)2021 Dec 01.
Article in English | MEDLINE | ID: covidwho-1542718
ABSTRACT
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21238045

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21238045