Literature DB >> 34131359

Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment.

Chellammal Surianarayanan1, Pethuru Raj Chelliah2.   

Abstract

The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use. © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2021.

Entities:  

Keywords:  Applications of AI; Artificial intelligence; COVID-19; Deep learning; Machine learning

Year:  2021        PMID: 34131359      PMCID: PMC8191724          DOI: 10.1007/s00354-021-00128-0

Source DB:  PubMed          Journal:  New Gener Comput        ISSN: 0288-3635            Impact factor:   1.048


  31 in total

1.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.

Authors:  Waseem Rawat; Zenghui Wang
Journal:  Neural Comput       Date:  2017-06-09       Impact factor: 2.026

Review 2.  How far have we come? Artificial intelligence for chest radiograph interpretation.

Authors:  K Kallianos; J Mongan; S Antani; T Henry; A Taylor; J Abuya; M Kohli
Journal:  Clin Radiol       Date:  2019-01-28       Impact factor: 2.350

3.  Improved protein structure prediction using potentials from deep learning.

Authors:  Andrew W Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Žídek; Alexander W R Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; Karen Simonyan; Steve Crossan; Pushmeet Kohli; David T Jones; David Silver; Koray Kavukcuoglu; Demis Hassabis
Journal:  Nature       Date:  2020-01-15       Impact factor: 49.962

4.  Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.

Authors:  Ashis Kumar Das; Shiba Mishra; Saji Saraswathy Gopalan
Journal:  PeerJ       Date:  2020-09-28       Impact factor: 2.984

Review 5.  Application of Artificial Intelligence in COVID-19 drug repurposing.

Authors:  Sweta Mohanty; Md Harun Ai Rashid; Mayank Mridul; Chandana Mohanty; Swati Swayamsiddha
Journal:  Diabetes Metab Syndr       Date:  2020-07-03

6.  Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.

Authors:  Tao Ai; Zhenlu Yang; Hongyan Hou; Chenao Zhan; Chong Chen; Wenzhi Lv; Qian Tao; Ziyong Sun; Liming Xia
Journal:  Radiology       Date:  2020-02-26       Impact factor: 11.105

7.  Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine.

Authors:  Arni S R Srinivasa Rao; Jose A Vazquez
Journal:  Infect Control Hosp Epidemiol       Date:  2020-03-03       Impact factor: 3.254

Review 8.  Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.

Authors:  Tarik Alafif; Abdul Muneeim Tehame; Saleh Bajaba; Ahmed Barnawi; Saad Zia
Journal:  Int J Environ Res Public Health       Date:  2021-01-27       Impact factor: 3.390

9.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Authors:  Lin Li; Lixin Qin; Zeguo Xu; Youbing Yin; Xin Wang; Bin Kong; Junjie Bai; Yi Lu; Zhenghan Fang; Qi Song; Kunlin Cao; Daliang Liu; Guisheng Wang; Qizhong Xu; Xisheng Fang; Shiqin Zhang; Juan Xia; Jun Xia
Journal:  Radiology       Date:  2020-03-19       Impact factor: 11.105

10.  AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.

Authors:  Bo Wang; Shuo Jin; Qingsen Yan; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Wei Xu; Xinghuan Wang; Jianming Wang; Zheng You; Jiahong Dong
Journal:  Appl Soft Comput       Date:  2020-11-10       Impact factor: 6.725

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  1 in total

1.  Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications.

Authors:  Aditya Gupta; Vibha Jain; Amritpal Singh
Journal:  New Gener Comput       Date:  2021-12-14       Impact factor: 1.180

  1 in total

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