Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

Múñoz, Lilia; Villarreal, Vladimir; Nielsen, Mel; Caballero, Yen; Sittón-Candanedo, Inés; Corchado, Juan M.

doi:10.3390/electronics10232901

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…Another popular example is the COVID-19 ZA South Africa Dashboard, developed at the University of Pretoria by the Data Science for Social Impact Research Group (DSFSI). In South America, especially in Panama, a dashboard termed the COVID-19 Open data was developed to track and predict cases [106]. Saudi Arabia launched its own dashboard in the Middle East to enable public authorities to monitor and combat COVID-19 cases.…”

Section: Covid-19 Datasetsmentioning

confidence: 99%

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

et al. 2022

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As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.

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Section: Covid-19 Datasetsmentioning

confidence: 99%

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

et al. 2022

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“…This study was based on the use of deep learning techniques. These techniques have been implemented for years in different topics, for example, classification networks, medical research, or pattern recognition [36][37][38][39]. Deep learning has been used as a precise tool for the classification of medical records, e.g., EEG, ECG.…”

Section: Introductionmentioning

confidence: 99%

High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network

et al. 2022

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Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.

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Deciphering the Complexities of COVID‐19‐Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework

Benameur,

Sassi,

Ouarda

et al. 2024

Int J Imaging Syst Tech

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The literature has widely described the interaction between cardiac complications and COVID‐19. However, the diagnosis of cardiac complications caused by COVID‐19 using Computed Tomography (CT) images remains a challenge due to the diverse clinical manifestations. To address this issue, this study proposes a novel configuration of Convolutional Neural Network (CNN) for detecting cardiac complications derived from COVID‐19 using CT images. The main contribution of this work lies in the use of CNN techniques in combination with Long Short‐Term Memory (LSTM) for cardiac complication detection. To explore two‐class classification (COVID‐19 without cardiac complications vs. COVID‐19 with cardiac complications), 10 650 CT images were collected from COVID‐19 patients with and without myocardial infarction, myocarditis, and arrhythmia. The information was annotated by two radiology specialists. A total of 0.926 was found to be the accuracy, 0.84 was the recall, 0.82 was the precision, 0.82 was the F1‐score, and 0.830 was the g‐mean of the suggested model. These results show that the suggested approach can identify cardiac problems from COVID‐19 in CT scans. Patients with COVID‐19 may benefit from the proposed LSTM‐CNN architecture's enhanced ability to identify cardiac problems.

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Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

Cited by 4 publications

References 42 publications

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network

Deciphering the Complexities of COVID‐19‐Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework

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