Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Alabool, Hamzeh; Alarabiat, Deemah; Habib, Mohammad Asadul; Al-Khasawneh, Ahmad; Abualigah, Laith; Shehab, Mohammad

doi:10.21203/rs.3.rs-30432/v1

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…The current paper complements the work of Wynants et al who have published a living systematic review 12 on publications and preprints of studies describing multivariable models for screening of COVID-19 infections in the general population, differential diagnosis of COVID-19 infection in patients that are symptomatic and prognostication in patients with this systematic review employed specialized quality metrics for the assessment of radiomics and deep learning-based diagnostic models in radiology. This is also in contrast to previous studies that have assessed AI algorithms in COVID-19 13,14 . Limitations of the current literature most frequently reflect either a limitation of the dataset used in the model or methodological mistakes repeated in many studies that probably lead to overly optimistic performance evaluations.…”

Section: Discussioncontrasting

confidence: 63%

“…While earlier reviews provided a broad analysis of predictive models for COVID-19 diagnosis and prognosis [12][13][14][15] , this Analysis highlights the unique challenges researchers face when developing classical machine learning and deep learning models using imaging data. This Analysis builds on the approach of Wynants et al 12 : we assess the risk of bias in the papers considered, going further by incorporating a quality screening stage to ensure only those papers with sufficiently documented methodologies are reviewed in most detail.…”

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confidence: 99%

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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Section: Discussioncontrasting

confidence: 63%

mentioning

confidence: 99%

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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“…In recent years, machine learning has driven advances in many different fields [ 12 , 13 , 14 ]. The process of predicting the sensory data is used not only for securing reliability of the existing data and defining the cause of faults that had already occurred also in forecasting future to detect the user’s risk in advance.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors

Cho

Lee

2020

Micromachines

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A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has not been given. Since TVOCs (total volatile organic compounds) cause serious harm to human health and plants, the prevention of such damages with a reduction in their occurrence frequency becomes not an optional process but an essential one in manufacturing, as well as for chemical industries and laboratories. In this study, with consideration of the characteristics of the machine learning technique and ICT (information and communications technology), TVOC sensors are explored as a function of grounded data analysis and the selection of machine learning models, determining their performance in real situations. For representative scenarios, considering features from an ICT semiconductor sensor and one targeting TVOC gas, we investigated suitable analysis methods and machine learning models such as LSTM (long short-term memory), GRU (gated recurrent unit), and RNN (recurrent neural network). Detailed factors for these machine learning models with respect to the concentration of TVOC gas in the atmosphere are compared with original sensory data to obtain their accuracy. From this work, we expect to significantly minimize risk in empirical applications, i.e., maintaining homeostasis or predicting abnormal situations to construct an opportune response.

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“…One of those issues that have persistently emerged lately is the COVID-19 pandemic. The literature has currently witnessed extensive research for attempting to produce various approaches and techniques that can contribute to managing and tackling the enormous and abrupt growth of the COVID-19 pandemic across the globe (Alabool et al, 2020). This pandemic has also emerged sporadically throughout the entire world (Cruz and Dias, 2020) and according to (Rothe et al, 2020) asymptomatic people represent possible infectious causes of such a pandemic where the entire transmission dynamics can change for it.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the AI domain, the evaluation of machine learning has played a significant role in the COVID-19 pandemic at its very early stages (Alabool et al, 2020). The aim of applying the machine-learning domain according to many researchers is to provide an analysis of the current cases that are produced by the pandemic and to study the inflectional effects within the upcoming days.…”

Section: Literature Reviewmentioning

confidence: 99%

A Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) Model

Atawneh¹,

Ghaleb²,

Hussein³

et al. 2020

Journal of Computer Science

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Reviews at present, different machine learning techniques and algorithms have been applied for predicting significant factors of the Coronavirus Disease-2019 (COVID-19) such as the outbreak and diagnosis. In this study, the most accurate time series forecasting model, namely, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the expected cumulative number of confirmed and critical cases in Saudi Arabia for the upcoming months. Additionally, the dataset is collected from the King Abdullah Petroleum Studies and Research Centre (KAPSARC). Acquiring the number of expected cases within a short period is considered crucial as it provides an important knowledge that can be applied by the health sector in containing the COVID-19 pandemic and forming the proper precautions and strategies that are concerned on the public health system. The main finding of this research is that the number of cumulative confirmed cases is expected to increase at a high rate in the upcoming two months, while the number of critical cases is forecasted to increase at a smaller rate compared to the total number of cases. To evaluate the performance of the adopted model, different statistical matrices as the R Squared, Mean Squarer Error, Root Mean Square Error and Mean Absolute Error are used in this research. It is found to be proven from the findings that the proposed model generates an accurate prediction of the expected number of cumulative confirmed and critical cases in the upcoming months.

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Artificial intelligence techniques for Containment COVID-19 Pandemic: A Systematic Review

Cited by 18 publications

References 7 publications

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors

A Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) Model

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