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Dekel D, Marchant A, Del Pozo Banos M, Mhereeg M, Lee SC, John A. Exploring the Views of Young People, Including Those With a History of Self-Harm, on the Use of Their Routinely Generated Data for Mental Health Research: Web-Based Cross-Sectional Survey Study. JMIR Ment Health 2025; 12:e60649. [PMID: 40073393 PMCID: PMC11947630 DOI: 10.2196/60649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 01/10/2025] [Accepted: 01/13/2025] [Indexed: 03/14/2025] Open
Abstract
BACKGROUND Secondary use of routinely collected health care data has great potential benefits in epidemiological studies primarily due to the large scale of preexisting data. OBJECTIVE This study aimed to engage respondents with and without a history of self-harm, gain insight into their views on the use of their data for research, and determine whether there were any differences in opinions between the 2 groups. METHODS We examined young people's views on the use of their routinely collected data for mental health research through a web-based survey, evaluating any differences between those with and without a history of self-harm. RESULTS A total of 1765 respondents aged 16 to 24 years were included. Respondents' views were mostly positive toward the use and linkage of their data for research purposes for public benefit, particularly with regard to the use of health care data (mental health or otherwise), and generally echoed existing evidence on the opinions of older age groups. Individuals who reported a history of self-harm and subsequently contacted health services more often reported being "extremely likely" or "likely" to share mental health data (contacted: 209/609, 34.3%; 95% CI 28.0-41.2; not contacted: 169/782, 21.6%; 95% CI 15.8-28.7) and physical health data (contacted: 117/609, 19.2%; 95% CI 12.7-27.8; not contacted: 96/782, 12.3%; 95% CI 6.7-20.9) compared with those who had not contacted services. Respondents were overall less likely to want to share their social media data, which they considered to be more personal compared to their health care data. Respondents stressed the importance of anonymity and the need for an appropriate ethical framework. CONCLUSIONS Young people are aware, and they care about how their data are being used and for what purposes, irrespective of having a history of self-harm. They are largely positive about the use of health care data (mental or physical) for research and generally echo the opinions of older age groups raising issues around data security and the use of data for the public interest.
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Affiliation(s)
- Dana Dekel
- Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Amanda Marchant
- Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | | | - Mohamed Mhereeg
- Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Sze Chim Lee
- Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Ann John
- Swansea University Medical School, Swansea University, Swansea, United Kingdom
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Pezzutti DL, Makary MS. Role of Imaging in Diagnosis and Management of COVID-19: Evidence-Based Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1457:237-246. [PMID: 39283430 DOI: 10.1007/978-3-031-61939-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.
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Affiliation(s)
- Dante L Pezzutti
- Department of Radiology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, 4th Floor, Columbus, OH, 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, 4th Floor, Columbus, OH, 43210, USA.
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Kierner S, Kucharski J, Kierner Z. Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review. J Biomed Inform 2023; 144:104428. [PMID: 37355025 DOI: 10.1016/j.jbi.2023.104428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/28/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making. PURPOSE This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems. METHODS PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting. RESULTS The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts. CONCLUSIONS Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research. OTHER A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.
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Affiliation(s)
- Slawomir Kierner
- Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, 27 Isabella Street, 02116 Boston, MA, USA.
| | - Jacek Kucharski
- Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, 18/22 Stefanowskiego St., 90-924 Łodź, Poland.
| | - Zofia Kierner
- University of California, Berkeley College of Letters & Science, Berkeley, CA 94720-1786, USA.
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Janoudi G, Fell DB, Ray JG, Foster AM, Giffen R, Clifford TJ, Rodger MA, Smith GN, Walker MC. Augmented Intelligence for Clinical Discovery in Hypertensive Disorders of Pregnancy Using Outlier Analysis. Cureus 2023; 15:e36909. [PMID: 37009347 PMCID: PMC10065308 DOI: 10.7759/cureus.36909] [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] [Accepted: 03/27/2023] [Indexed: 04/04/2023] Open
Abstract
Objectives Clinical discoveries are heralded by observing unique and unusual clinical cases. The effort of identifying such cases rests on the shoulders of busy clinicians. We assess the feasibility and applicability of an augmented intelligence framework to accelerate the rate of clinical discovery in preeclampsia and hypertensive disorders of pregnancy-an area that has seen little change in its clinical management. Methods We conducted a retrospective exploratory outlier analysis of participants enrolled in the folic acid clinical trial (FACT, N=2,301) and the Ottawa and Kingston birth cohort (OaK, N=8,085). We applied two outlier analysis methods: extreme misclassification contextual outlier and isolation forest point outlier. The extreme misclassification contextual outlier is based on a random forest predictive model for the outcome of preeclampsia in FACT and hypertensive disorder of pregnancy in OaK. We defined outliers in the extreme misclassification approach as mislabelled observations with a confidence level of more than 90%. Within the isolation forest approach, we defined outliers as observations with an average path length z score less or equal to -3, or more or equal to 3. Content experts reviewed the identified outliers and determined if they represented a potential novelty that could conceivably lead to a clinical discovery. Results In the FACT study, we identified 19 outliers using the isolation forest algorithm and 13 outliers using the random forest extreme misclassification approach. We determined that three (15.8%) and 10 (76.9%) were potential novelties, respectively. Out of 8,085 participants in the OaK study, we identified 172 outliers using the isolation forest algorithm and 98 outliers using the random forest extreme misclassification approach; four (2.3%) and 32 (32.7%), respectively, were potential novelties. Overall, the outlier analysis part of the augmented intelligence framework identified a total of 302 outliers. These were subsequently reviewed by content experts, representing the human part of the augmented intelligence framework. The clinical review determined that 49 of the 302 outliers represented potential novelties. Conclusions Augmented intelligence using extreme misclassification outlier analysis is a feasible and applicable approach for accelerating the rate of clinical discoveries. The use of an extreme misclassification contextual outlier analysis approach has resulted in a higher proportion of potential novelties than using the more traditional point outlier isolation forest approach. This finding was consistent in both the clinical trial and real-world cohort study data. Using augmented intelligence through outlier analysis has the potential to speed up the process of identifying potential clinical discoveries. This approach can be replicated across clinical disciplines and could exist within electronic medical records systems to automatically identify outliers within clinical notes to clinical experts.
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Affiliation(s)
- Ghayath Janoudi
- Epidemiology and Public Health, University of Ottawa, Ottawa, CAN
- Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, CAN
| | - Deshayne B Fell
- Epidemiology and Public Health, University of Ottawa, Ottawa, CAN
- Maternal and Neonatal Research, Children's Hospital of Eastern Ontario, Ottawa, CAN
| | - Joel G Ray
- Medicine, Health Policy Management and Evaluation, and Obstetrics and Gynecology, Saint Michael's Hospital, Toronto, CAN
| | | | - Randy Giffen
- Medical Research, International Business Machines (IBM) Corporation, Ottawa, CAN
| | - Tammy J Clifford
- Research, Canadian Institute of Health Research, Ottawa, CAN
- Epidemiology and Public Health, University of Ottawa, Ottawa, CAN
| | - Marc A Rodger
- Medicine, McGill University, Montreal, CAN
- Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, CAN
| | - Graeme N Smith
- Obstetrics and Gynecology, Kingston General Hospital, Kingston, CAN
- Biomedical and Molecular Sciences, Queen's University, Kingston, CAN
| | - Mark C Walker
- Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, CAN
- Epidemiology and Public Health, University of Ottawa, Ottawa, CAN
- Maternal and Nenonatal Research, University of Ottawa, Ottawa, CAN
- Obstetrics and Gynecology, University of Ottawa, Ottawa, CAN
- Obstetrics, Gynecology, and Newborn Care, The Ottawa Hospital, Ottawa, CAN
- Maternal and Nenonatal Research, Children's Hospital of Eastern Ontario, Ottawa, CAN
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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Khounraz F, Khodadoost M, Gholamzadeh S, Pourhamidi R, Baniasadi T, Jafarbigloo A, Mohammadi G, Ahmadi M, Ayyoubzadeh SM. Prognosis of COVID-19 patients using lab tests: A data mining approach. Health Sci Rep 2023; 6:e1049. [PMID: 36628109 PMCID: PMC9826741 DOI: 10.1002/hsr2.1049] [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: 09/04/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 01/10/2023] Open
Abstract
Background The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques. Methods In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.
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Affiliation(s)
- Fariba Khounraz
- Administration and Resources Development AffairsShahid Beheshti University of Medical SciencesTehranIran
| | - Mahmood Khodadoost
- School of Traditional Medicine, Traditional Medicine & Materia Medical Research CenterShahid Beheshti University of Medical SciencesTehranIran
| | - Saeid Gholamzadeh
- Administration and Resources Development AffairsShahid Beheshti University of Medical SciencesTehranIran
- Legal Medicine Research Center, Legal Medicine OrganizationTehranIran
| | - Rashed Pourhamidi
- Non Communicable Diseases Research Center, Bam University of Medical SciencesBamIran
| | - Tayebeh Baniasadi
- Department of Health Information Technology, Faculty of Para‐MedicineHormozgan University of Medical SciencesBandar AbbasIran
| | - Aida Jafarbigloo
- Department of Health Information Technology, School of Allied Medical SciencesShahid Beheshti University of Medical SciencesTehranIran
| | - Gohar Mohammadi
- Administration and Resources Development AffairsShahid Beheshti University of Medical SciencesTehranIran
| | - Mahnaz Ahmadi
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of PharmacyShahid Beheshti University of Medical SciencesTehranIran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
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7
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Alexi A, Rosenfeld A, Lazebnik T. A Security Games Inspired Approach for Distributed Control Of Pandemic Spread. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Ariel Alexi
- Department of Information Science Bar‐Ilan University Ramat‐Gan Israel
| | - Ariel Rosenfeld
- Department of Information Science Bar‐Ilan University Ramat‐Gan Israel
| | - Teddy Lazebnik
- Department of Cancer Biology Cancer Institute University College London London UK
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Ozsahin DU, Isa NA, Uzun B. The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis. Diagnostics (Basel) 2022; 12:2943. [PMID: 36552949 PMCID: PMC9777320 DOI: 10.3390/diagnostics12122943] [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: 10/15/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Sciences, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Nuhu Abdulhaqq Isa
- Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Biomedical Engineering, College of Health Science and Technology, Keffi 961101, Keffi Nasarawa State, Nigeria
| | - Berna Uzun
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Statistics, Carlos III Madrid University, 28903 Getafe, Madrid, Spain
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8131193. [PMID: 35991144 PMCID: PMC9391156 DOI: 10.1155/2022/8131193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/28/2022] [Indexed: 12/04/2022]
Abstract
The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.
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Li G, Zhang X, Zhang G. How the 5G Enabled the COVID-19 Pandemic Prevention and Control: Materiality, Affordance, and (De-)Spatialization. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19158965. [PMID: 35897336 PMCID: PMC9332237 DOI: 10.3390/ijerph19158965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 12/04/2022]
Abstract
5G, the most disruptive innovation, had played a significant role in the COVID-19 pandemic prevention and control. However, as a novel technology and context, we have little knowledge about how 5G enabled pandemic prevention and control. This study collected 212 cases and conducted qualitative research to explore how the 5G worked in prevention and control. Based on the concepts of materiality and affordance, we grounded two affordances of spatialization and de-spatialization from the data. Spatialization provides non-contact ways to complete the tasks which are supposed to be completed in contact, and de-spatialization provides remote operations to complete the tasks which are supposed to be completed on-site. Spatialization and de-spatialization enabled the diagnosis and treatment of the infectors to relieve the unbalance of medical staff, cutting the infectious route to contain the viral spread, and logistic supply to support the prevention and control. Our study offers theoretical contributions to digital pandemic prevention and control, and the literature on 5G also offers practical implications.
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Magruk A. The Desirable Systemic Uncertainty in Complex IoT Sensor Networks-General Anticipatory Foresight Perspective. SENSORS 2022; 22:s22051698. [PMID: 35270844 PMCID: PMC8914765 DOI: 10.3390/s22051698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 02/01/2023]
Abstract
A wide methodological spectrum regarding future research is offered by anticipation studies, with a special role of foresight studies. Many studies of this type focus on generating the desired future, taking into account the fact that it is accompanied by uncertainty. The author of this publication postulates treating uncertainty as an equivalent—in relation to the future—research object. This approach allows us to formulate general assumptions for a model of the anticipatory management of systemic uncertainty in IoT networks. The goal of such a model will not be to eliminate or even minimize uncertainty, but to regulate it to a desired level. Such an action can bring many more benefits than trying to zero out uncertainty. On the general side, uncertainty can be studied in two ways: (1) as an abstract idea, or (2) as a feature of a particular structure, also with elements of research on its abstract component. In this paper the attention is focused on the second approach. The main research area is the IoT network in its broadest sense, with a particular role of the social construct, in the context of the study of systemic uncertainty in relation to selected anticipatory actions. The overarching goal is to define a desired state, or to determine what such a desired state is, of anticipatory IoT uncertainty.
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Affiliation(s)
- Andrzej Magruk
- Faculty of Engineering Management, Bialystok University of Technology, 45A, Wiejska Street, 15-351 Bialystok, Poland
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12
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Alali Y, Harrou F, Sun Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Sci Rep 2022; 12:2467. [PMID: 35165290 PMCID: PMC8844088 DOI: 10.1038/s41598-022-06218-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/24/2022] [Indexed: 12/13/2022] Open
Abstract
This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.
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Affiliation(s)
- Yasminah Alali
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Fouzi Harrou
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| | - Ying Sun
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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Abstract
In this chapter, we mainly focus on the use of AI-driven tools for COVID-19 predictive modeling, screening, and decision-making. We first discuss prediction models, their merits, and pitfalls. We then review deep learning models for COVID-19 detection and/or screening (with experiments) by taking different dataset sizes into account, which is followed by a conclusive study on how big data is big. The chapter provides a journey of deep neural networks for lung abnormality screening, where we consider COVID-19 as a particular case.
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A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks. APPL INTELL 2021; 51:2740-2763. [PMID: 34764560 PMCID: PMC7609830 DOI: 10.1007/s10489-020-02019-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2020] [Indexed: 12/17/2022]
Abstract
In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.
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Sharma M. Drone Technology for Assisting COVID-19 Victims in Remote Areas: Opportunity and Challenges. J Med Syst 2021; 45:85. [PMID: 34322759 PMCID: PMC8318630 DOI: 10.1007/s10916-021-01759-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/16/2021] [Indexed: 11/24/2022]
Affiliation(s)
- Manik Sharma
- Department of CSA, DAV University, Jalandhar, India.
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16
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Umar Ibrahim A, Pwavodi PC, Ozsoz M, Al-Turjman F, Galaya T, Agbo JJ. Crispr biosensing and Ai driven tools for detection and prediction of Covid-19. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1952652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
| | - Pwadubashiyi Coston Pwavodi
- Department of Biomedical Engineering, Near East University, Nicosia, Turkey
- Department of Artificial Intelligence, Near East University, Nicosia, Turkey
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, Turkey
- Department of Medical Biology and Genetics, Near East University, Nicosia, Turkey
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Center for AI and IoT, Near East University, Mersin, Turkey
| | - Tirah Galaya
- Department of Medical Biology and Genetics, Near East University, Nicosia, Turkey
| | - Joy Johnson Agbo
- Department of Nursing, Cyprus International University, Nicosia, Turkey
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17
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Yasar H, Ceylan M. Deep Learning-Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images. Cognit Comput 2021:1-28. [PMID: 34306240 PMCID: PMC8280590 DOI: 10.1007/s12559-021-09915-9] [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: 07/06/2020] [Accepted: 07/07/2021] [Indexed: 11/10/2022]
Abstract
Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9676, 0.9181, 0.9456, 0.9545, and 0.9890, respectively; using pipeline approaches, the values were 0.9832, 0.9622, 0.9577, 0.9642, and 0.9923, respectively. For COVID-19 pneumonia/other pneumonia classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9615, 0.7270, 0.8846, 0.9180, and 0.9370, respectively; using pipeline approaches, the values were 0.9915, 0.8140, 0.9071, 0.9327, and 0.9615, respectively. The results of this study show that classification success can be increased by reducing the time to obtain per-image results through using the proposed pipeline approaches.
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Affiliation(s)
- Huseyin Yasar
- Ministry of Health of Republic of Turkey, Ankara, Turkey
| | - Murat Ceylan
- Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey
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18
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2021; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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19
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Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech 2021. [PMID: 33522669 DOI: 10.1002/jemt] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Ibrahim Abunadi
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Amjad Rehman Khan
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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20
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Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J Radiol 2021. [DOI: 10.4329/wjr.v13.i6.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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21
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Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J Radiol 2021; 13:171-191. [PMID: 34249238 PMCID: PMC8245752 DOI: 10.4329/wjr.v13.i6.171] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/15/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023] Open
Abstract
The role of radiology and the radiologist have evolved throughout the coronavirus disease-2019 (COVID-19) pandemic. Early on, chest computed tomography was used for screening and diagnosis of COVID-19; however, it is now indicated for high-risk patients, those with severe disease, or in areas where polymerase chain reaction testing is sparsely available. Chest radiography is now utilized mainly for monitoring disease progression in hospitalized patients showing signs of worsening clinical status. Additionally, many challenges at the operational level have been overcome within the field of radiology throughout the COVID-19 pandemic. The use of teleradiology and virtual care clinics greatly enhanced our ability to socially distance and both are likely to remain important mediums for diagnostic imaging delivery and patient care. Opportunities to better utilize of imaging for detection of extrapulmonary manifestations and complications of COVID-19 disease will continue to arise as a more detailed understanding of the pathophysiology of the virus continues to be uncovered and identification of predisposing risk factors for complication development continue to be better understood. Furthermore, unidentified advancements in areas such as standardized imaging reporting, point-of-care ultrasound, and artificial intelligence offer exciting discovery pathways that will inevitably lead to improved care for patients with COVID-19.
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Affiliation(s)
- Dante L Pezzutti
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
| | - Vibhor Wadhwa
- Department of Radiology, Weill Cornell Medical Center, New York City, NY 10065, United States
| | - Mina S Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
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22
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Surianarayanan C, Chelliah PR. Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment. NEW GENERATION COMPUTING 2021; 39:717-741. [PMID: 34131359 PMCID: PMC8191724 DOI: 10.1007/s00354-021-00128-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/05/2021] [Indexed: 05/15/2023]
Abstract
The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.
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Affiliation(s)
- Chellammal Surianarayanan
- Government Arts and Science College (Formerly Bharathidasan University Constituent Arts and Science College), Affiliated to Bharathidasan University, Tiruchirappalli, Tamilnadu India
| | - Pethuru Raj Chelliah
- Site Reliability Engineering Division, Reliance Jio Platforms Ltd, Bangalore, India
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23
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Hammoudi K, Benhabiles H, Melkemi M, Dornaika F, Arganda-Carreras I, Collard D, Scherpereel A. Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. J Med Syst 2021; 45:75. [PMID: 34101042 PMCID: PMC8185498 DOI: 10.1007/s10916-021-01745-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 05/11/2021] [Indexed: 11/26/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.
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Affiliation(s)
- Karim Hammoudi
- Department of Computer Science, IRIMAS, Université de Haute-Alsace, 68100 Mulhouse, France
- Université de Strasbourg, Strasbourg, France
| | - Halim Benhabiles
- UMR 8520 - IEMN - Institut d’Electronique de Microélectronique et de Nanotechnologie, Université Lille, CNRS, Centrale Lille, Université Polytechnique Hauts-de-France, Junia, F-59000 Lille, France
| | - Mahmoud Melkemi
- Department of Computer Science, IRIMAS, Université de Haute-Alsace, 68100 Mulhouse, France
- Université de Strasbourg, Strasbourg, France
| | - Fadi Dornaika
- Department of Computer Science & Artificial Intelligence, University of the Basque Country, 20018 San Sebastián, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Ignacio Arganda-Carreras
- Department of Computer Science & Artificial Intelligence, University of the Basque Country, 20018 San Sebastián, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
- Donostia International Physics Center (DIPC), 20018 San Sebastian, Spain
| | - Dominique Collard
- LIMMS/CNRS-IIS UMI 2820, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba Meguro Ku, Tokyo, 153-8505 Japan
- CNRS/IIS/COL/Lille 1 SMMiL-E Project, CNRS Délégation Nord-Pas-de-Calais et Picardie, 2 rue des Canonniers, Lille, Cedex 59046 France
| | - Arnaud Scherpereel
- Lille University Hospital (CHU Lille), French National Institute of Health and Medical Research (Inserm), University of Lille, U1189 - ONCO-THAI, 59000 Lille, France
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Dairi A, Harrou F, Zeroual A, Hittawe MM, Sun Y. Comparative study of machine learning methods for COVID-19 transmission forecasting. J Biomed Inform 2021; 118:103791. [PMID: 33915272 PMCID: PMC8074522 DOI: 10.1016/j.jbi.2021.103791] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 03/17/2021] [Accepted: 04/05/2021] [Indexed: 12/16/2022]
Abstract
Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.
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Affiliation(s)
- Abdelkader Dairi
- University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science department Signal, Image and Speech Laboratory (SIMPA) Laboratory, El Mnaouar, BP 1505, Bir El Djir 31000, Oran, Algeria.
| | - Fouzi Harrou
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
| | - Abdelhafid Zeroual
- Faculty of Technology, Department of electrical engineering, University of 20 August 1955, Skikda 21000, Algeria; LAIG Laboratory, University of 08 May 1945, Guelma 24000, Algeria
| | - Mohamad Mazen Hittawe
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Ying Sun
- King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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Navaz AN, Serhani MA, El Kassabi HT, Al-Qirim N, Ismail H. Trends, Technologies, and Key Challenges in Smart and Connected Healthcare. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:74044-74067. [PMID: 34812394 PMCID: PMC8545204 DOI: 10.1109/access.2021.3079217] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 05/04/2023]
Abstract
Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.
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Affiliation(s)
- Alramzana Nujum Navaz
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Mohamed Adel Serhani
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software EngineeringCollege of Information TechnologyUAE UniversityAl AinUnited Arab Emirates
| | - Nabeel Al-Qirim
- Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates
| | - Heba Ismail
- Department of Computer Science and Information Technology (CS-IT)College of EngineeringAbu Dhabi UniversityAl AinUnited Arab Emirates
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COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083414] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues.
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Irfan M, Iftikhar MA, Yasin S, Draz U, Ali T, Hussain S, Bukhari S, Alwadie AS, Rahman S, Glowacz A, Althobiani F. Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:3056. [PMID: 33809665 PMCID: PMC8002268 DOI: 10.3390/ijerph18063056] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/28/2021] [Accepted: 03/04/2021] [Indexed: 12/28/2022]
Abstract
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as 'hybrid images' (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
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Affiliation(s)
- Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia; (M.I.); (A.S.A.); (S.R.)
| | - Muhammad Aksam Iftikhar
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan;
| | - Sana Yasin
- Department of Computer Science, University of OKara, Okara 56130, Pakistan;
| | - Umar Draz
- Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan; (U.D.); (S.H.)
| | - Tariq Ali
- Computer Science Department, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan
| | - Shafiq Hussain
- Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan; (U.D.); (S.H.)
| | - Sarah Bukhari
- Department of Computer Science, National Fertilizer Corporation Institute of Engineering and Technology, Multan 60000, Pakistan;
| | - Abdullah Saeed Alwadie
- Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia; (M.I.); (A.S.A.); (S.R.)
| | - Saifur Rahman
- Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia; (M.I.); (A.S.A.); (S.R.)
| | - Adam Glowacz
- Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland;
| | - Faisal Althobiani
- Faculty of Maritime Studies, King Abdulaziz University, Jeddah 21577, Saudi Arabia;
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Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech 2021; 84:1462-1474. [PMID: 33522669 PMCID: PMC8014446 DOI: 10.1002/jemt.23702] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/27/2020] [Accepted: 12/27/2020] [Indexed: 12/13/2022]
Abstract
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.
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Affiliation(s)
- Tanzila Saba
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Ibrahim Abunadi
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Amjad Rehman Khan
- Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
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An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11031238] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components—aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were −768, −780, and −798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (−34 HU) compared to the other two methods (−114 and −87 HU). The maximum gradient on the data method was applied to quantify the three components in our population—the aerated, intermediate, and consolidation components showed median values of 793 ± 499 cc, 914 ± 291 cc, and 126 ± 111 cc, respectively, while the median value of the first peak was −853 ± 56 HU.
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Al-Emran M, Al-Kabi MN, Marques G. A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic. STUDIES IN SYSTEMS, DECISION AND CONTROL 2021:1-8. [DOI: 10.1007/978-3-030-67716-9_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Yasar H, Ceylan M. A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:5423-5447. [PMID: 33041635 PMCID: PMC7537375 DOI: 10.1007/s11042-020-09894-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 09/05/2020] [Accepted: 09/16/2020] [Indexed: 05/15/2023]
Abstract
The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0,9197, 0,9891, 0,9473, 0,9058, 0,9888; respectively for 2-fold cross-validation, and they were 0,9404, 0,9901, 0,9599, 0,9284, 0,9903; respectively for 10-fold cross-validation.
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Affiliation(s)
- Huseyin Yasar
- Ministry of Health of Republic of Turkey, Ankara, Turkey
| | - Murat Ceylan
- Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey
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32
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Al-Emran M, Arpaci I. Intelligent Systems and Novel Coronavirus (COVID-19): A Bibliometric Analysis. STUDIES IN SYSTEMS, DECISION AND CONTROL 2021:59-67. [DOI: 10.1007/978-3-030-67716-9_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Ni Q, Sun ZY, Qi L, Chen W, Yang Y, Wang L, Zhang X, Yang L, Fang Y, Xing Z, Zhou Z, Yu Y, Lu GM, Zhang LJ. A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. Eur Radiol 2020; 30:6517-6527. [PMID: 32617690 PMCID: PMC7331494 DOI: 10.1007/s00330-020-07044-9] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/06/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.
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Affiliation(s)
- Qianqian Ni
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Zhi Yuan Sun
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Li Qi
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Wen Chen
- Department of Medical Imaging, Taihe Hospital, Shiyan, 442008, Hubei, China
| | - Yi Yang
- Department of Medical Imaging, Wuhan First Hospital, Wuhan, 430022, Hubei, China
| | - Li Wang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Xinyuan Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Liu Yang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Yi Fang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | | | - Zhen Zhou
- School of Electronics Engineering and Computer Science, Peking University, Beijing, 10080, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China
| | - Long Jiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
- Department of Medical Imaging, Medical Imaging Center, Nanjing Clinical School, Southern Medical University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China.
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Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res 2020; 22:e21476. [PMID: 32946413 PMCID: PMC7595751 DOI: 10.2196/21476] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/25/2020] [Accepted: 09/15/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. OBJECTIVE To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. METHODS We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. RESULTS In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. CONCLUSIONS In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.
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Affiliation(s)
- Jiayang Chen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kay Choong See
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
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35
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Khoury MJ, Armstrong GL, Bunnell RE, Cyril J, Iademarco MF. The intersection of genomics and big data with public health: Opportunities for precision public health. PLoS Med 2020; 17:e1003373. [PMID: 33119581 PMCID: PMC7595300 DOI: 10.1371/journal.pmed.1003373] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.
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Affiliation(s)
- Muin J. Khoury
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Gregory L. Armstrong
- Office of Advanced Molecular Detection, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Rebecca E. Bunnell
- Office of Science, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Juliana Cyril
- Office of Technology and Innovation, Office of Science, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Michael F. Iademarco
- Center for Surveillance, Epidemiology and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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36
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Kpozehouen EB, Chen X, Zhu M, Macintyre CR. Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study. JMIR Public Health Surveill 2020; 6:e18939. [PMID: 32598290 PMCID: PMC7505682 DOI: 10.2196/18939] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 05/11/2020] [Accepted: 06/29/2020] [Indexed: 12/15/2022] Open
Abstract
Background The coronavirus disease (COVID-19) outbreak in China was first reported to the World Health Organization (WHO) on December 31, 2019, and the first cases were officially identified around December 8, 2019. Although the origin of COVID-19 has not been confirmed, approximately half of the early cases were linked to a seafood market in Wuhan. However, the first two documented patients did not visit the seafood market. News reports, social media, and informal sources may provide information about outbreaks prior to formal notification. Objective The aim of this study was to identify early signals of pneumonia or severe acute respiratory illness (SARI) in China prior to official recognition of the COVID-19 outbreak in December 2019 using open-source data. Methods To capture early reports, we searched an open source epidemic observatory, EpiWatch, for SARI or pneumonia-related illnesses in China from October 1, 2019. The searches were conducted using Google and the Chinese search engine Baidu. Results There was an increase in reports following the official notification of COVID-19 to the WHO on December 31, 2019, and a report that appeared on December 26, 2019 was retracted. A report of severe pneumonia on November 22, 2019, in Xiangyang was identified, and a potential index patient was retrospectively identified on November 17. Conclusions The lack of reports of SARI outbreaks prior to December 31, 2019, with a retracted report on December 26, suggests media censorship, given that formal reports indicate that cases began appearing on December 8. However, the findings also support a relatively recent origin of COVID-19 in November 2019. The case reported on November 22 was transferred to Wuhan approximately one incubation period before the first identified cases on December 8; this case should be further investigated, as only half of the early cases were exposed to the seafood market in Wuhan. Another case of COVID-19 has since been retrospectively identified in Hubei on November 17, 2019, suggesting that the infection was present prior to December.
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Affiliation(s)
- Elizabeth Benedict Kpozehouen
- Biosecurity Program, The Kirby Institute for Infection and Immunity, University of New South Wales, Sydney, Australia
| | - Xin Chen
- Biosecurity Program, The Kirby Institute for Infection and Immunity, University of New South Wales, Sydney, Australia
| | - Mengyao Zhu
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | - C Raina Macintyre
- Biosecurity Program, The Kirby Institute for Infection and Immunity, University of New South Wales, Sydney, Australia
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Kaufman AE, Naidu S, Ramachandran S, Kaufman DS, Fayad ZA, Mani V. Review of radiographic findings in COVID-19. World J Radiol 2020; 12:142-155. [PMID: 32913561 PMCID: PMC7457163 DOI: 10.4329/wjr.v12.i8.142] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/06/2020] [Accepted: 08/16/2020] [Indexed: 02/06/2023] Open
Abstract
The purpose of this study is to review the published literature for the range of radiographic findings present in patients suffering from coronavirus disease 2019 infection. This novel corona virus is currently the cause of a worldwide pandemic. Pulmonary symptoms and signs dominate the clinical picture and radiologists are called upon to evaluate chest radiographs (CXR) and computed tomography (CT) images to assess for infiltrates and to define their extent, distribution and progression. Multiple studies attempt to characterize the disease course by looking at the timing of imaging relative to the onset of symptoms. In general, plain CXR show bilateral disease with a tendency toward the lung periphery and have an appearance most consistent with viral pneumonia. Chest CT images are most notable for showing bilateral and peripheral ground glass and consolidated opacities and are marked by an absence of concomitant pulmonary nodules, cavitation, adenopathy and pleural effusions. Published literature mentioning organ systems aside from pulmonary manifestations are relatively less common, yet present and are addressed in this review. Similarly, publications focusing on imaging modalities aside from CXR and chest CT are sparse in this evolving crisis and are likewise addressed in this review. The role of imaging is examined as it is currently being debated in the medical community, which is not at all surprising considering the highly infectious nature of Severe Acute Respiratory Syndrome coronavirus 2.
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Affiliation(s)
- Audrey E Kaufman
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
| | - Sonum Naidu
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
| | - Sarayu Ramachandran
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
| | - Dalia S Kaufman
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
| | - Zahi A Fayad
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
| | - Venkatesh Mani
- Department of Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Hess Center for Science and Medicine, New York, NY 10029, United States
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Abstract
For COVID-19, predictive modeling, in the literature, uses broadly SEIR/SIR, agent-based, curve-fitting techniques/models. Besides, machine-learning models that are built on statistical tools/techniques are widely used. Predictions aim at making states and citizens aware of possible threats/consequences. However, for COVID-19 outbreak, state-of-the-art prediction models are failed to exploit crucial and unprecedented uncertainties/factors, such as a) hospital settings/capacity; b) test capacity/rate (on a daily basis); c) demographics; d) population density; e) vulnerable people; and f) income versus commodities (poverty). Depending on what factors are employed/considered in their models, predictions can be short-term and long-term. In this paper, we discuss how such continuous and unprecedented factors lead us to design complex models, rather than just relying on stochastic and/or discrete ones that are driven by randomly generated parameters. Further, it is a time to employ data-driven mathematically proved models that have the luxury to dynamically and automatically tune parameters over time.
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Affiliation(s)
- K C Santosh
- Department of Computer Science, University of South Dakota, 414 E Clark St, Vermillion, SD, 57069, USA.
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Güneyli S, Atçeken Z, Doğan H, Altınmakas E, Atasoy KÇ. Radiological approach to COVID-19 pneumonia with an emphasis on chest CT. Diagn Interv Radiol 2020; 26:323-332. [PMID: 32352917 PMCID: PMC7360081 DOI: 10.5152/dir.2020.20260] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.
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Affiliation(s)
- Serkan Güneyli
- From the Department of Radiology (S.G. ), Koc University School of Medicine, Istanbul, Turkey
| | - Zeynep Atçeken
- From the Department of Radiology (S.G. ), Koc University School of Medicine, Istanbul, Turkey
| | - Hakan Doğan
- From the Department of Radiology (S.G. ), Koc University School of Medicine, Istanbul, Turkey
| | - Emre Altınmakas
- From the Department of Radiology (S.G. ), Koc University School of Medicine, Istanbul, Turkey
| | - Kayhan Çetin Atasoy
- From the Department of Radiology (S.G. ), Koc University School of Medicine, Istanbul, Turkey
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40
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Abstract
Initially recognized of COVID-19 within the world in 2019, the World Health Organization situational report from May 22nd, 2020, globally, there is a complete of 5,204,508 confirmed cases, with 212 countries being affected by the novel coronavirus. 2019 novel coronavirus (SARS-CoV-2) is that the seventh member of the family of coronaviruses is enveloped viruses with a positive sense, single-stranded RNA genome. The SARS-CoV-2 may be a �-CoV of group 2B there is 70% comparability in genetic sequence to SARS-CoV. The source of the new coronavirus infection has been resolved as bats. With whole-genome sequences of SARS-CoV-2 is 96% comparatively at the whole-genome level to a bat coronavirus. Mechanisms of transmission are concluded to incorporate contact, droplet, and possibly airborne under certain circumstances supported ancient experiences associated with SARS-CoV outbreaks. Although antiretroviral therapy is being widely used everywhere the globe for such patents, effects at finding a SARS-CoV vaccine haven�t succeeded so far.
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Albahri AS, Hamid RA, Alwan JK, Al-Qays ZT, Zaidan AA, Zaidan BB, Albahri AOS, AlAmoodi AH, Khlaf JM, Almahdi EM, Thabet E, Hadi SM, Mohammed KI, Alsalem MA, Al-Obaidi JR, Madhloom HT. Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review. J Med Syst 2020; 44:122. [PMID: 32451808 PMCID: PMC7247866 DOI: 10.1007/s10916-020-01582-x] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 04/27/2020] [Indexed: 01/28/2023]
Abstract
Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.
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Affiliation(s)
- A S Albahri
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Rula A Hamid
- College of Business Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq
| | - Jwan K Alwan
- Biomedical Informatics College/University of Information Technology and Communications (UOITC), Baghdad, Iraq
| | - Z T Al-Qays
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - A A Zaidan
- Department of Computing, FSKIK, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia.
| | - B B Zaidan
- Department of Computing, FSKIK, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | - A O S Albahri
- Department of Computing, FSKIK, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | - A H AlAmoodi
- Department of Computing, FSKIK, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | | | - E M Almahdi
- General Secretariat for the Council of Ministers (GSCOM), Baghdad, Iraq
| | - Eman Thabet
- Department of Computer Science, College of Education for Pure Sciences, University of Basra, Basra, Iraq
| | - Suha M Hadi
- Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - K I Mohammed
- Department of Computing, FSKIK, Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia
| | - M A Alsalem
- Department of Management Information System, College of Administration and Economic, University of Mosul, Mosul, Iraq
| | - Jameel R Al-Obaidi
- Department of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, Iraq
| | - H T Madhloom
- Information Technology Department, College of Applied Sciences, Ministry of Higher Education, Muscat, Iraq
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Boukhris M, Hillani A, Moroni F, Annabi MS, Addad F, Ribeiro MH, Mansour S, Zhao X, Ybarra LF, Abbate A, Vilca LM, Azzalini L. Cardiovascular Implications of the COVID-19 Pandemic: A Global Perspective. Can J Cardiol 2020; 36:1068-1080. [PMID: 32425328 PMCID: PMC7229739 DOI: 10.1016/j.cjca.2020.05.018] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/04/2020] [Accepted: 05/10/2020] [Indexed: 12/16/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), represents the pandemic of the century, with approximately 3.5 million cases and 250,000 deaths worldwide as of May 2020. Although respiratory symptoms usually dominate the clinical presentation, COVID-19 is now known to also have potentially serious cardiovascular consequences, including myocardial injury, myocarditis, acute coronary syndromes, pulmonary embolism, stroke, arrhythmias, heart failure, and cardiogenic shock. The cardiac manifestations of COVID-19 might be related to the adrenergic drive, systemic inflammatory milieu and cytokine-release syndrome caused by SARS-CoV-2, direct viral infection of myocardial and endothelial cells, hypoxia due to respiratory failure, electrolytic imbalances, fluid overload, and side effects of certain COVID-19 medications. COVID-19 has profoundly reshaped usual care of both ambulatory and acute cardiac patients, by leading to the cancellation of elective procedures and by reducing the efficiency of existing pathways of urgent care, respectively. Decreased use of health care services for acute conditions by non-COVID-19 patients has also been reported and attributed to concerns about acquiring in-hospital infection. Innovative approaches that leverage modern technologies to tackle the COVID-19 pandemic have been introduced, which include telemedicine, dissemination of educational material over social media, smartphone apps for case tracking, and artificial intelligence for pandemic modelling, among others. This article provides a comprehensive overview of the pathophysiology and cardiovascular implications of COVID-19, its impact on existing pathways of care, the role of modern technologies to tackle the pandemic, and a proposal of novel management algorithms for the most common acute cardiac conditions.
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Affiliation(s)
- Marouane Boukhris
- Division of Cardiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Ali Hillani
- Division of Cardiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | | | - Mohamed Salah Annabi
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université de Laval, Québec, Québec, Canada
| | - Faouzi Addad
- Division of Cardiology, Abderrahmen Mami Hospital, Ariana, Tunisia
| | | | - Samer Mansour
- Division of Cardiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
| | - Xiaohui Zhao
- Institute of Cardiovascular Research, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Luiz Fernando Ybarra
- London Health Sciences Centre, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Antonio Abbate
- Division of Cardiology, VCU Pauley Heart Center and Wright Center for Clinical and Translationa Research, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Luz Maria Vilca
- Department of Obstetrics and Gynecology, Buzzi Hospital-ASST Fatebenefratelli Sacco, University of Milan, Milan, Italy
| | - Lorenzo Azzalini
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Uddin M, Mustafa F, Rizvi TA, Loney T, Al Suwaidi H, Al-Marzouqi AHH, Kamal Eldin A, Alsabeeha N, Adrian TE, Stefanini C, Nowotny N, Alsheikh-Ali A, Senok AC. SARS-CoV-2/COVID-19: Viral Genomics, Epidemiology, Vaccines, and Therapeutic Interventions. Viruses 2020; 12:E526. [PMID: 32397688 PMCID: PMC7290442 DOI: 10.3390/v12050526] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/07/2020] [Accepted: 05/07/2020] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 pandemic is due to infection caused by the novel SARS-CoV-2 virus that impacts the lower respiratory tract. The spectrum of symptoms ranges from asymptomatic infections to mild respiratory symptoms to the lethal form of COVID-19 which is associated with severe pneumonia, acute respiratory distress, and fatality. To address this global crisis, up-to-date information on viral genomics and transcriptomics is crucial for understanding the origins and global dispersion of the virus, providing insights into viral pathogenicity, transmission, and epidemiology, and enabling strategies for therapeutic interventions, drug discovery, and vaccine development. Therefore, this review provides a comprehensive overview of COVID-19 epidemiology, genomic etiology, findings from recent transcriptomic map analysis, viral-human protein interactions, molecular diagnostics, and the current status of vaccine and novel therapeutic intervention development. Moreover, we provide an extensive list of resources that will help the scientific community access numerous types of databases related to SARS-CoV-2 OMICs and approaches to therapeutics related to COVID-19 treatment.
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Affiliation(s)
- Mohammed Uddin
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Farah Mustafa
- Department of Biochemistry, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE; (F.M.); (A.H.H.A.-M.)
| | - Tahir A. Rizvi
- Department of Microbiology & Immunology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE;
| | - Tom Loney
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
| | - Hanan Al Suwaidi
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
| | - Ahmed H. Hassan Al-Marzouqi
- Department of Biochemistry, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE; (F.M.); (A.H.H.A.-M.)
| | - Afaf Kamal Eldin
- Department of Food, Nutrition and Health, United Arab Emirates University, Al Ain, UAE;
| | | | - Thomas E. Adrian
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
| | - Cesare Stefanini
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, UAE;
| | - Norbert Nowotny
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
- Viral Zoonoses, Emerging and Vector-Borne Infections Group, Institute of Virology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Alawi Alsheikh-Ali
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
| | - Abiola C. Senok
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE; (M.U.); (T.L.); (H.A.S.); (T.E.A.); (N.N.)
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Park HW, Park S, Chong M. Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. J Med Internet Res 2020; 22:e18897. [PMID: 32325426 PMCID: PMC7202309 DOI: 10.2196/18897] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/16/2020] [Accepted: 04/22/2020] [Indexed: 11/22/2022] Open
Abstract
Background SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). Conclusions Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.
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Affiliation(s)
- Han Woo Park
- Department of Media & Communication, Interdisciplinary Graduate Programs of Digital Convergence Business and East Asian Cultural Studies, Yeungnam University, Gyeongsan-si, Republic of Korea.,Cyber Emotions Research Institute, Gyeongsan-si, Republic of Korea
| | - Sejung Park
- Tim Russert Department of Communication, John Carroll University, Cleveland Heights, OH, United States
| | - Miyoung Chong
- College of Information, University of North Texas, Denton, TX, United States
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Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, Liu H. Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease. Acad Radiol 2020; 27:603-608. [PMID: 32204987 PMCID: PMC7156150 DOI: 10.1016/j.acra.2020.03.003] [Citation(s) in RCA: 170] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 02/07/2023]
Abstract
Coronavirus disease is an emerging infection caused by a novel coronavirus that is moving rapidly. High resolution computed tomography (CT) allows objective evaluation of the lung lesions, thus enabling us to better understand the pathogenesis of the disease. With serial CT examinations, the occurrence, development, and prognosis of the disease can be better understood. The imaging can be sorted into four phases: early phase, progressive phase, severe phase, and dissipative phase. The CT appearance of each phase and temporal progression of the imaging findings are demonstrated.
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Affiliation(s)
- Mingzhi Li
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Pinggui Lei
- Department of Radiology, The Affifiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Bingliang Zeng
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Zongliang Li
- Department of Radiology, Nanfeng County People's Hospital, Fuzhou, China
| | - Peng Yu
- Radiology Department, Jinxian County People's Hospital, Nanchang, China
| | - Bing Fan
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China.
| | - Chuanhong Wang
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Zicong Li
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Jian Zhou
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Shaobo Hu
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
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Lv M, Luo X, Estill J, Liu Y, Ren M, Wang J, Wang Q, Zhao S, Wang X, Yang S, Feng X, Li W, Liu E, Zhang X, Wang L, Zhou Q, Meng W, Qi X, Xun Y, Yu X, Chen Y. Coronavirus disease (COVID-19): a scoping review. Euro Surveill 2020; 25:2000125. [PMID: 32317050 PMCID: PMC7175649 DOI: 10.2807/1560-7917.es.2020.25.15.2000125] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 03/16/2020] [Indexed: 12/13/2022] Open
Abstract
BackgroundIn December 2019, a pneumonia caused by a novel coronavirus (SARS-CoV-2) emerged in Wuhan, China and has rapidly spread around the world since then.AimThis study aims to understand the research gaps related to COVID-19 and propose recommendations for future research.MethodsWe undertook a scoping review of COVID-19, comprehensively searching databases and other sources to identify literature on COVID-19 between 1 December 2019 and 6 February 2020. We analysed the sources, publication date, type and topic of the retrieved articles/studies.ResultsWe included 249 articles in this scoping review. More than half (59.0%) were conducted in China. Guidance/guidelines and consensuses statements (n = 56; 22.5%) were the most common. Most (n = 192; 77.1%) articles were published in peer-reviewed journals, 35 (14.1%) on preprint servers and 22 (8.8%) posted online. Ten genetic studies (4.0%) focused on the origin of SARS-CoV-2 while the topics of molecular studies varied. Nine of 22 epidemiological studies focused on estimating the basic reproduction number of COVID-19 infection (R0). Of all identified guidance/guidelines (n = 35), only ten fulfilled the strict principles of evidence-based practice. The number of articles published per day increased rapidly until the end of January.ConclusionThe number of articles on COVID-19 steadily increased before 6 February 2020. However, they lack diversity and are almost non-existent in some study fields, such as clinical research. The findings suggest that evidence for the development of clinical practice guidelines and public health policies will be improved when more results from clinical research becomes available.
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Affiliation(s)
- Meng Lv
- School of Public Health, Lanzhou University, Lanzhou, China
- These authors contributed equally to this work and share first authorship
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Xufei Luo
- School of Public Health, Lanzhou University, Lanzhou, China
- These authors contributed equally to this work and share first authorship
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Janne Estill
- Institute of Global Health, University of Geneva, Geneva, Switzerland
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland
| | - Yunlan Liu
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Mengjuan Ren
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Jianjian Wang
- School of Public Health, Lanzhou University, Lanzhou, China
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Qi Wang
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Siya Zhao
- School of Public Health, Lanzhou University, Lanzhou, China
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Xiaohui Wang
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Shu Yang
- College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xixi Feng
- School of Public Health, Chengdu Medical College, Chengdu, China
| | - Weiguo Li
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Department of Respiratory Diseases, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Enmei Liu
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Department of Respiratory Diseases, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xianzhuo Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Ling Wang
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Qi Zhou
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Wenbo Meng
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Xiaolong Qi
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Yangqin Xun
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Xuan Yu
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yaolong Chen
- World Health Organization (WHO) Collaborating Centre for Guideline Implementation and Knowledge Translation, Lanzhou, China
- Guideline International Network Asia, Lanzhou, China
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou, China
- Lanzhou University, an affiliate of the Cochrane China Network, Lanzhou, China
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
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Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease. Acad Radiol 2020. [PMID: 32204987 DOI: 10.1016/j.acra.2020.03.003.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Coronavirus disease is an emerging infection caused by a novel coronavirus that is moving rapidly. High resolution computed tomography (CT) allows objective evaluation of the lung lesions, thus enabling us to better understand the pathogenesis of the disease. With serial CT examinations, the occurrence, development, and prognosis of the disease can be better understood. The imaging can be sorted into four phases: early phase, progressive phase, severe phase, and dissipative phase. The CT appearance of each phase and temporal progression of the imaging findings are demonstrated.
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48
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Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst 2020; 44:93. [PMID: 32189081 PMCID: PMC7087612 DOI: 10.1007/s10916-020-01562-1] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/17/2020] [Indexed: 12/19/2022]
Abstract
The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.
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Affiliation(s)
- K C Santosh
- Department of Computer Science, University of South Dakota, 414 E Clark St, Vermillion, SD, 57069, USA.
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