Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic

被引：20

作者：

Gopinath, Nishanth ^{[1
]}

机构：

[1] SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY 14260 USA

来源：

PROCESS BIOCHEMISTRY | 2021年 / 110卷

关键词：

Artificial intelligence; Coronavirus; Machine learning; Deep learning; Epidemic; DIAGNOSIS;

D O I：

10.1016/j.procbio.2021.08.001

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Artificial intelligence (AI), a method of simulating the human brain in order to complete tasks in a more effective manner, has had numerous implementations in fields from manufacturing sectors to digital electronics. Despite the potential of AI, it may be obstinate to assume that the person administered society would rely solely on AI; with an example being the healthcare field. With the ever-expanding discoveries made on a regular basis regarding the growth of various diseases and its preservations, utilizing brain power may be deemed essential, but that doesn't leave AI as a redundant asset. With the years of accumulated data regarding patterns and the analysis of various medical circumstances, algorithms can be formed, which could further assist in situations such as diagnosis support and population health management. This matter becomes even more relevant in today's society with the currently ongoing COVID-19 pandemic by SARS-CoV-2. With the uncertainty of this pandemic from strain variants to the rolling speeds of vaccines, AI could be utilized to our advantage in order to assist us with the fight against COVID-19. This review briefly discusses the application of AI in the COVID-19 situation for various health benefits.

引用

页码：94 / 99

页数：6

共 34 条

[1] COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images [J].

Afshar, Parnian ;

Heidarian, Shahin ;

Naderkhani, Farnoosh ;

Oikonomou, Anastasia ;

Plataniotis, Konstantinos N. ;

Mohammadi, Arash .

PATTERN RECOGNITION LETTERS, 2020, 138 :638-643

[2]

[Anonymous], 2013, J Health Med Inform, DOI DOI 10.4172/2157-7420.1000124

[3] Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks [J].

Apostolopoulos, Ioannis D. ;

Mpesiana, Tzani A. .

PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2020, 43 (02) :635-640

[4] A Novel Deep Learning Approach for Automated Diagnosis of Acute Ischemic Infarction on Computed Tomography [J].

Beecy, Ashley N. ;

Chang, Qi ;

Anchouche, Khalil ;

Baskaran, Lohendran ;

Elmore, Kimberly ;

Kolli, Kranthi ;

Wang, Hao ;

Al'Aref, Subhi ;

Pena, Jessica M. ;

Knight-Greenfield, Ashley ;

Patel, Praneil ;

Sun, Peng ;

Zhang, Tong ;

Kamel, Hooman ;

Gupta, Ajay ;

Min, James K. .

JACC-CARDIOVASCULAR IMAGING, 2018, 11 (11) :1723-1725

[5] Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study [J].

Betancur, Julian ;

Commandeur, Frederic ;

Motlagh, Mahsaw ;

Sharir, Tali ;

Einstein, Andrew J. ;

Bokhari, Sabahat ;

Fish, Mathews B. ;

Ruddy, Terrence D. ;

Kaufmann, Philipp ;

Sinusas, Albert J. ;

Miller, Edward J. ;

Bateman, Timothy M. ;

Dorbala, Sharmila ;

Di Carli, Marcelo ;

Germano, Guido ;

Otaki, Yuka ;

Tamarappoo, Balaji K. ;

Dey, Damini ;

Berman, Daniel S. ;

Slomka, Piotr J. .

JACC-CARDIOVASCULAR IMAGING, 2018, 11 (11) :1654-1663

[6]

Borkowski Andrew A, 2020, Fed Pract, V37, P398, DOI 10.12788/fp.0045

[7] Artificial intelligence in the diagnosis of cardiovascular disease [J].

Campos Zeron, Rubens Moura ;

Serrano Junior, Carlos Vicente .

REVISTA DA ASSOCIACAO MEDICA BRASILEIRA, 2019, 65 (12) :1438-1441

[8] Artificial intelligence for the diagnosis of heart failure [J].

Choi, Dong-Ju ;

Park, Jin Joo ;

Ali, Taqdir ;

Lee, Sungyoung .

NPJ DIGITAL MEDICINE, 2020, 3 (01)

[9] Detecting breast cancer using artificial intelligence: Convolutional neural network [J].

Choudhury, Avishek ;

Perumalla, Sunanda .

TECHNOLOGY AND HEALTH CARE, 2021, 29 (01) :33-43

[10] A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain [J].

Ding, Yuming ;

Sohn, Jae Ho ;

Kawczynski, Michael G. ;

Trivedi, Hari ;

Harnish, Roy ;

Jenkins, Nathaniel W. ;

Lituiev, Dmytro ;

Copeland, Timothy P. ;

Aboian, Mariam S. ;

Aparici, Carina Mari ;

Behr, Spencer C. ;

Flavell, Robert R. ;

Huang, Shih-Ying ;

Zalocusky, Kelly A. ;

Nardo, Lorenzo ;

Seo, Youngho ;

Hawkins, Randall A. ;

Pampaloni, Miguel Hernandez ;

Hadley, Dexter ;

Franc, Benjamin L. .

RADIOLOGY, 2019, 290 (02) :456-464

← 1 2 3 4 →