The Role of Artificial Intelligence in Tackling COVID-19

Arora, Neelima; Banerjee, Amit Kumar; Narasu, M. Laxmi

doi:10.2217/fvl-2020-0130

Cited by 85 publications

(63 citation statements)

References 60 publications

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“…For example, Google DeepMind introduced Alphafold system to find protein structure of COVID-19 virus with Deep Residual Network. Furthermore, the AI models are helpful in analyzing the potential mutations of the virus and corresponding identifying the best possible candidates for vaccine design [46].…”

Section: Ai For Study Of Protein Structure and Vaccine Developmentmentioning

confidence: 99%

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

Surianarayanan

Raj

2021

New Gener. Comput.

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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.

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Section: Ai For Study Of Protein Structure and Vaccine Developmentmentioning

confidence: 99%

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

Surianarayanan

Raj

2021

New Gener. Comput.

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“…In recent years, AI-based algorithms were found to be on par with or even more accurate than human experts in diagnosing diseases and drug discovery [7][8][9].…”

Section: Novel Digital Technologiesmentioning

confidence: 99%

Future developments in training

Power

Dellière

Velikov

et al. 2021

Clinical Microbiology and Infection

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…The research institutes are now trying to counter this pandemic through innovative solutions. Several preprint research articles have been published recently, demonstrating various techniques to detect COVID-19 through chest X-ray images [ 24 , 25 ]. Deep convolutional network was used by [ 24 ] to detect COVID-19 positive cases using chest images and achieved the classification accuracy of 91.62 %.…”

Section: Introductionmentioning

confidence: 99%

“…Deep convolutional network was used by [ 24 ] to detect COVID-19 positive cases using chest images and achieved the classification accuracy of 91.62 %. Neelima Arora et al [ 25 ] investigated the role of AI in diagnostics of COVID-19. Nevertheless, the performance various models was not investigated for multiclass classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Almalki

Qayyum²,

Irfan

et al. 2021

Healthcare

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The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

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The Role of Artificial Intelligence in Tackling COVID-19

Abstract: AI can be harnessed for forecasting the spread of virus and developing early warning systems by extracting information from social media platforms, calls and news sites and provide useful information about the vulnerable regions and for prediction of morbidity and mortality "

Cited by 85 publications

References 60 publications

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

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

Future developments in training

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

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