Using artificial intelligence technology to fight COVID-19: a review

Peng, Yong; Liu, Enbin; Peng, Shanbi; Chen, Qikun; Li, Dangjian; Lian, Dianpeng

doi:10.1007/s10462-021-10106-z

Cited by 30 publications

(21 citation statements)

References 143 publications

(119 reference statements)

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“…Supplementary Table S1 shows the terms used in this phase. Additionally, due to the effect of COVID-19 on the number of publications both in the cardiothoracic imaging field in general and also AI research, we included the term “COVID-19” to encompass the latest advancements in this field [ 24 ]. The search was confined to the period 1 January 2012 to 31 May 2022 to be inclusive of the most recent advances in the field.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

et al. 2022

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Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

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Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

et al. 2022

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“…In an organized population, epidemiological dynamics can be quite complicated. Extensive research literature on detection and spread dynamics of COVID‐19 from chest X‐ray images were written by Tayarani (2021), Ghaderzadeh et al (2021), Peng et al (2022), Khan et al (2021), and Sufian et al (2020). The important articles are only included here related to neural models for detecting, segmenting, and predicting COVID‐19 patients.…”

Section: Applications Of ML In Solving Dynamical Problemsmentioning

confidence: 99%

Machine intelligence in dynamical systems: \A state‐of‐art review

Sahoo

Chakraverty

2022

WIREs Data Min & Knowl

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This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional‐link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self‐adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Application Areas > Science and Technology

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“…This significant decline was noted on the 5th and 6th of April, 2020. The IHME COVID-19 health services use forecasting team created a similar model, [10]. This model predicts the effect of Covid-19 on hospital beds and ventilator demand in the United States.…”

Section: 𝑎 + 𝑏 𝑒𝑟𝑓(𝑐𝑥 + 𝑑)mentioning

confidence: 99%

Cmbatting COVID-19: Artificial Intelligence Technologies & Challenges

Patel

Trivedi²

2022

Preprint

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AI works proficiently to emulate human intellect. It may also play an important role in understanding and recommending the creation of a COVID-19 vaccine. This outcome-driven technology is utilized for effective screening, assessing, forecasting, and tracking of present and potential future patients. Traditional network designs are unable to cope calmly with the impact of COVID-19 due to massive network data traffic and resource optimization requirements. As indicated by the growing amount of restorative clinical data, artificial intelligence (AI) has the potential to successfully boost the upper limit of the medical and health network. We discuss the primary uses of artificial intelligence technology in the process of suppressing the coronavirus from three main perspectives: prediction, symptom detection, and development, based on an extensive literature study. Furthermore, the advancement of next-generation network (NGN) technologies based on machine learning (ML) has given limitless opportunities for the formation of novel medical approaches. We have also discussed the challenges related to AI technologies in combatting COVID-19. The devastating epidemic of the Novel Coronavirus (Covid-19) has highlighted the importance of accurate prediction mathematical models. We have also discussed different mathematical models, their predictive capabilities, drawbacks, and practical validity.

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Using artificial intelligence technology to fight COVID-19: a review

Cited by 30 publications

References 143 publications

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Machine intelligence in dynamical systems: \A state‐of‐art review

Cmbatting COVID-19: Artificial Intelligence Technologies & Challenges

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