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Dobrijević D, Vilotijević-Dautović G, Katanić J, Horvat M, Horvat Z, Pastor K. Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023; 15:1522. [PMID: 37515208 PMCID: PMC10383367 DOI: 10.3390/v15071522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Gordana Vilotijević-Dautović
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jasmina Katanić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Mirjana Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Zoltan Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Kristian Pastor
- Faculty of Technology, University of Novi Sad, 21000 Novi Sad, Serbia
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Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives. Biomed Signal Process Control 2023; 83:104642. [PMID: 36818992 PMCID: PMC9917176 DOI: 10.1016/j.bspc.2023.104642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 11/29/2022] [Accepted: 01/25/2023] [Indexed: 02/12/2023]
Abstract
In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies.
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Smadi AA, Abugabah A, Al-Smadi AM, Almotairi S. SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans. INFORMATICS IN MEDICINE UNLOCKED 2022; 32:101059. [PMID: 36033909 PMCID: PMC9398554 DOI: 10.1016/j.imu.2022.101059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/06/2022] Open
Abstract
COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). We include a global average pooling layer, flattening, and two dense layers that are fully connected. The model’s effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model’s performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew’s correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic.
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Affiliation(s)
- Ahmad Al Smadi
- School of Artificial Intelligence, Xidian University, No. 2 South Taibai Road, Xian, 710071, China.,College of Technological Innovation, Zayed University, Abu Dhabi Campus, UAE
| | - Ahed Abugabah
- College of Technological Innovation, Zayed University, Abu Dhabi Campus, UAE
| | - Ahmad Mohammad Al-Smadi
- Department of Computer Science, Al-Balqa Applied University, Ajloun University College, Jordan
| | - Sultan Almotairi
- Faculty of Community College, Majmaah University, Al Majma'ah, Saudi Arabia
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Ragab M, Alshehri S, Alhakamy NA, Mansour RF, Koundal D. Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6185013. [PMID: 35634055 PMCID: PMC9135545 DOI: 10.1155/2022/6185013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/30/2022] [Accepted: 04/12/2022] [Indexed: 01/09/2023]
Abstract
It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
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Affiliation(s)
- Mahmoud Ragab
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Mathematics, Al-Azhar University, Nasercity 11884, Cairo, Egypt
| | - Samah Alshehri
- Department of Pharmacy Practice, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nabil A. Alhakamy
- Department of Pharmaceutics, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Mohamed Saeed Tamer Chair for Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, New Valley University, El-Kharga 72511, Egypt
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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Ghayvat H, Awais M, Bashir AK, Pandya S, Zuhair M, Rashid M, Nebhen J. AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia. Neural Comput Appl 2022; 35:14591-14609. [PMID: 35250181 PMCID: PMC8886865 DOI: 10.1007/s00521-022-07055-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 01/31/2022] [Indexed: 12/16/2022]
Abstract
A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
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Affiliation(s)
- Hemant Ghayvat
- Innovation Division, Technical University of Denmark, Lyngby, Denmark
- Department of Computer Science and Media Technology, E-health Unit (Improved Data to and from Patients), Linnaeus University, Vaxjo, Sweden
- Building Realization and Robotics, Technical University of Munich, Munich, Germany
| | - Muhammad Awais
- Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433 China
| | - A. K. Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
- School of Information and Communication Engineering, University of Electronics Science and Technology of China (UESTC), Chengdu, China
| | - Sharnil Pandya
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharashtra India
| | - Mohd Zuhair
- Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat India
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India
| | - Jamel Nebhen
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
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Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031117. [PMID: 33513984 PMCID: PMC7908539 DOI: 10.3390/ijerph18031117] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/16/2021] [Accepted: 01/17/2021] [Indexed: 12/13/2022]
Abstract
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
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Affiliation(s)
- Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
- Correspondence:
| | - Abdul Muneeim Tehame
- Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan;
| | - Saleh Bajaba
- Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Ahmed Barnawi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Saad Zia
- IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia;
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