Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

El-Rashidy, Nora; Abdelrazik, Samir; Abuhmed, Tamer; Amer, Eslam; Ali, Farman; Hu, Jong-Wan; El–Sappagh, Shaker

doi:10.3390/diagnostics11071155

Cited by 49 publications

(36 citation statements)

References 292 publications

(311 reference statements)

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“…Consequently, the informativeness of given behavioral parameters is determined not by the number of measured patients, but by the total number of BPIV measurements. BPIV data presented in this study exceeds the most part of statistical studies with a confirmed diagnosis of COVID-19 32,33 . Most of the medical studies on COVID-19 diagnosis based on several hundred analysis data 32,33,34 , while the preliminary study includes data from more than 500,000 measurements of 40 behavioral parameters, and the basic study more than 150,000 measurements of 40 behavioral parameters.…”

Section: Discussionmentioning

confidence: 48%

“…BPIV data presented in this study exceeds the most part of statistical studies with a confirmed diagnosis of COVID-19 32,33 . Most of the medical studies on COVID-19 diagnosis based on several hundred analysis data 32,33,34 , while the preliminary study includes data from more than 500,000 measurements of 40 behavioral parameters, and the basic study more than 150,000 measurements of 40 behavioral parameters. Of course, an increase in the amount of data (relative to presented results) may lead to some change in the accuracy of diagnostics, but these changes are unlikely to be of a fundamental nature.…”

Section: Discussionmentioning

confidence: 48%

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COVID-19 Diagnosis by 5-second Facial Video Processing Using Vibraimage and Artificial Intelligence

Minkin¹,

Akimov²

2021

Preprint

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The Covid-19 pandemic spreads in waves for a year and a half, despite significant worldwide efforts, the development of biochemical diagnostic methods and population vaccination. One of the reasons for the infection spread is the impossibility of early disease detection through biochemical diagnostics, since biochemical processes slowly develop in a body. At the same time, well known that behavioral characteristics of a person, measured based on reflex movements, are capable for inertialess assessment of psychophysiological parameters. Vibraimage technology is the method of head micromovements video processing by inter-frame difference accumulation and converting spatial and temporal characteristics of the inter-frame difference into behavioral and psychophysiological parameters. Here we shown that behavioral parameters measured by vibraimage changed during COVID-19 infection. The identification of changes signs in behavioral parameters detected by AI trained on patients and controls. The best diagnostic accuracy (higher 94%) obtained using instantaneous values of behavioral parameters measured with the following vibraimage settings: 10Hz frequency of basic measurements; 25 inter-frame difference accumulations and averaging the diagnostic results over period of at least 5 seconds. COVID-19 diagnoses by behavioral parameters showed earlier (5-7 days) detection of the disease compared to symptoms and positive results of biochemical RT-PCR testing. Proposed method for COVID-19 diagnosis indicates infected persons within 5 seconds video processing using standard television cameras (web, IP) and computers, allows mass testing/selftesting and will stop the pandemic spread. We assume that head micromovements analysis for diagnosis of various diseases is possible not only with the help of vibraimage technology. Further research of human head micromovement analysis will help stop the COVID-19 pandemic and will contribute to the development of new contactless and environmentally friendly methods for early diagnosis of diseases.

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Section: Discussionmentioning

confidence: 48%

Section: Discussionmentioning

confidence: 48%

COVID-19 Diagnosis by 5-second Facial Video Processing Using Vibraimage and Artificial Intelligence

Minkin¹,

Akimov²

2021

Preprint

View full text Add to dashboard Cite

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“…There is no doubt that the world has been facing one of its biggest challenges since the World Health Organization (WHO) declared coronavirus disease (COVID-19) as an epidemic in December 2019 [ 1 ]. The consequences of this epidemic did not affect only the health domain, but also many areas such as the economy, education, and technology [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…The consequences of this epidemic did not affect only the health domain, but also many areas such as the economy, education, and technology [ 2 ]. COVID-19 is caused by a coronavirus known as severe acute respiratory syndrome two (SARS-CoV-2), which was first identified in Wuhan, China [ 1 , 3 , 4 ]. It is not a new disease, but it is the evolution of an older virus known as SARS-CoV that was also discovered in China in 2003 [ 1 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Osman

Saleh

et al. 2021

IJERPH

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Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

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Introducing PneumNet—A Groundbreaking Dual Version Deep Learning Model for Pneumonia Disease Detection

Aurangzeb,

Jamil,

Alhussein

2024

Int J Imaging Syst Tech

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The Internet of Medical Things (IoMT) has revolutionized healthcare, particularly in ambient assisted living (AAL). Deep learning has emerged as a powerful tool for identifying disorders and making health‐related decisions. Pneumonia, a dangerous and contagious disease, has a significant global impact. Prompt and accurate diagnosis is crucial, but traditional methods are time‐consuming and require specialized expertise. This research introduces PneumNet, a novel deep‐learning model. PneumNet consists of two versions: PneumNet v1.0 and PneumNet v2.0. The comparative analysis demonstrates PneumNet's exceptional performance. The top model achieves 99.84% accuracy, 99.87% F1‐score, 99.74% sensitivity, 100% specificity, 100% positive predictive value (PPV), and 99.58% negative predictive value (NPV). PneumNet outperforms other methods, accurately diagnosing pneumonia and improving treatment outcomes. By leveraging deep convolutional neural networks (D‐CNNs), PneumNet provides an efficient and accurate solution for pneumonia detection. These findings highlight the significance of D‐CNNs, particularly the proposed PneumNet model, in enhancing pneumonia detection accuracy and reducing mortality rates. IoMT and deep learning pave the way for transformative advancements in healthcare.

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Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Cited by 49 publications

References 292 publications

COVID-19 Diagnosis by 5-second Facial Video Processing Using Vibraimage and Artificial Intelligence

COVID-19 Diagnosis by 5-second Facial Video Processing Using Vibraimage and Artificial Intelligence

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Introducing PneumNet—A Groundbreaking Dual Version Deep Learning Model for Pneumonia Disease Detection

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