An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application DOI Creative Commons
Bakr Ahmed Taha, Yousif Al Mashhadany, Mohd Hadri Hafiz Mokhtar

и другие.

Sensors, Год журнала: 2020, Номер 20(23), С. 6764 - 6764

Опубликована: Ноя. 26, 2020

Timely detection and diagnosis are essentially needed to guide outbreak measures infection control. It is vital improve healthcare quality in public places, markets, schools airports provide useful insights into the technological environment help researchers acknowledge choices gaps available this field. In narrative review, of coronavirus disease 2019 (COVID-19) technologies summarized discussed with a comparison between them from several aspects arrive at an accurate decision on feasibility applying best these techniques biosensors that operate using laser technology. The collection data analysis was done by six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web Science, Google Scholar PubMed. This review includes three highlights: evaluating hazard pandemic COVID-19 transmission styles comparing Severe Acute Respiratory Syndrome (SARS) Middle East (MERS) identify main causes virus spreading, critical diagnose based artificial intelligence CT scans CXR images types biosensors. Finally, we select methods can potentially stop propagation pandemic.

Язык: Английский

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution DOI Creative Commons
Musa Ndiaye, Stephen S. Oyewobi, Adnan M. Abu‐Mahfouz

и другие.

IEEE Access, Год журнала: 2020, Номер 8, С. 186821 - 186839

Опубликована: Янв. 1, 2020

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to general way of life. Major sectors world industry and economy have been affected Internet Things (IoT) management framework is no exception in this regard. This article provides an up date survey on how pandemic such COVID-19 IoT technologies. It looks at contributions that associated sensor technologies made towards virus tracing, tracking spread mitigation. challenges deployment hardware face rapidly spreading looked into part review article. effects evolution architectures also addressed, leading likely outcomes future implementations. In general, insight advancement sensor-based E-health pandemics. answers question shaped networks.

Язык: Английский

Процитировано

167
Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19 DOI Creative Commons
Maria Tsikala Vafea, Eleftheria Atalla,

Joanna Georgakas

и другие.

Cellular and Molecular Bioengineering, Год журнала: 2020, Номер 13(4), С. 249 - 257

Опубликована: Июнь 24, 2020

The COVID-19 pandemic has caused an unprecedented health and economic worldwide crisis. Innovative solutions are imperative given limited resources immediate need for medical supplies, healthcare support treatments. purpose of this review is to summarize emerging technologies being implemented in the study, diagnosis, treatment COVID-19. Key focus areas include applications artificial intelligence, use Big Data Internet Things, importance mathematical modeling predictions, utilization technology community screening, nanotechnology vaccine development, utility telemedicine, implementation 3D-printing manage new demands potential robotics. concludes by highlighting collaboration scientific with open sharing knowledge, tools, expertise.

Язык: Английский

Процитировано

153
Artificial Intelligence for COVID-19: A Systematic Review DOI Creative Commons

Lian Wang,

Yonggang Zhang, Dongguang Wang

и другие.

Frontiers in Medicine, Год журнала: 2021, Номер 8

Опубликована: Сен. 30, 2021

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation epidemic trends, prognosis, exploration effective safe drugs vaccines; discusses potential limitations. Methods: We report this systematic review following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. searched PubMed, Embase Cochrane Library from inception 19 September 2020 published studies AI applications COVID-19. used PROBAST (prediction model risk bias assessment tool) assess quality literature related diagnosis prognosis registered protocol (PROSPERO CRD42020211555). Results: included 78 studies: 46 articles discussed AI-assisted COVID-19 with total accuracy 70.00 99.92%, sensitivity 73.00 100.00%, specificity 25 area under curve 0.732 1.000. Fourteen evaluated based on clinical characteristics at hospital admission, such as clinical, laboratory radiological characteristics, reaching 74.4 95.20%, 72.8 98.00%, 55 96.87% AUC 0.66 0.997 predicting critical Nine models predict peak, infection rate, number infected cases, transmission laws, development trend. Eight explore drugs, primarily through drug repurposing development. Finally, 1 article predicted vaccine targets that have develop vaccines. Conclusions: In review, we shown achieved high performance evaluation, prediction discovery enhance significantly existing medical healthcare system efficiency during pandemic.

Язык: Английский

Процитировано

139
Using artificial intelligence to improve public health: a narrative review DOI Creative Commons
David B. Olawade,

Ojima J. Wada,

Aanuoluwapo Clement David-Olawade

и другие.

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Окт. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Язык: Английский

Процитировано

111
Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review DOI
Carmela Comito, Clara Pizzuti

Artificial Intelligence in Medicine, Год журнала: 2022, Номер 128, С. 102286 - 102286

Опубликована: Март 28, 2022

Язык: Английский

Процитировано

97
Machine learning applications for COVID-19 outbreak management DOI Open Access
Arash Heidari, Nima Jafari Navimipour, Mehmet Ünal

и другие.

Neural Computing and Applications, Год журнала: 2022, Номер 34(18), С. 15313 - 15348

Опубликована: Июнь 10, 2022

Язык: Английский

Процитировано

96
IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain DOI
Omaji Samuel, Akogwu Blessing Omojo, Abdulkarim Musa Onuja

и другие.

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2022, Номер 27(2), С. 823 - 834

Опубликована: Янв. 18, 2022

Internet of medical things (IoMT) has made it possible to collect applications and devices improve healthcare information technology. Since the advent pandemic coronavirus (COVID-19) in 2019, public health become more sensitive than ever. Moreover, different news items incorporated have resulted differing perceptions COVID-19, especially on social media platform infrastructure. In addition, unprecedented virality changing nature COVID-19 makes call centres be likely overstressed, which is due a lack authentic unregulated information. Furthermore, data privacy restricted sharing among institutions. To resolve above-mentioned limitations, this paper proposing infrastructure based federated learning blockchain. The proposed potentials enhance trust authenticity disseminate Also, can effectively provide shared model while preserving owners. security analyses show that robust against security-related attacks.

Язык: Английский

Процитировано

85
The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns DOI
Yaou Hu, Hyounae Min

International Journal of Hospitality Management, Год журнала: 2023, Номер 110, С. 103437 - 103437

Опубликована: Фев. 10, 2023

Язык: Английский

Процитировано

65
Multivariate risks assessment for complex bio-systems by Gaidai-Xing reliability method DOI Creative Commons
Oleg Gaidai, Vladimir Yakimov, Qingsong Hu

и другие.

Systems and Soft Computing, Год журнала: 2024, Номер 6, С. 200074 - 200074

Опубликована: Янв. 24, 2024

Spread of novel coronavirus and other flu-like illnesses, periodically causing increased death morbidity rates, places pressures on national health systems. In order to provide a reliable long-term forecast the new infection rate, this research employs Gaidai-Xing bio-system reliability technique, especially suitable for multi-regional biological, environmental public The goal study was directly apply state art statistical techniques unprocessed raw clinical data, utilizing multicenter, population-based biostatistical methodology. Epidemiological risks have been accurately forecasted, specifically European Union member states. Based their survey suggested spatiotemporal methodology may be applied in variety biological applications.

Язык: Английский

Процитировано

34
Artificial Intelligence for COVID-19: Rapid Review DOI Creative Commons
Jiayang Chen, Kay Choong See

Journal of Medical Internet Research, Год журнала: 2020, Номер 22(10), С. e21476 - e21476

Опубликована: Сен. 16, 2020

COVID-19 was first discovered in December 2019 and has since evolved into a pandemic.To address this global health crisis, artificial intelligence (AI) been deployed at various levels of the care system. However, AI both potential benefits limitations. We therefore conducted review applications for COVID-19.We performed an extensive search PubMed EMBASE databases COVID-19-related English-language studies published between 1, 2019, March 31, 2020. supplemented database with reference list checks. A thematic analysis narrative conducted.In total, 11 papers were included review. applied to four areas: diagnosis, public health, clinical decision making, therapeutics. identified several limitations including insufficient data, omission multimodal methods AI-based assessment, delay realization benefits, poor internal/external validation, inability be used by laypersons, resource-poor settings, presence ethical pitfalls, legal barriers. could potentially explored other surveillance, combination big operation core services, management patients COVID-19.In view continuing increase number cases, given that multiple waves infections may occur, there is need effective help control pandemic. Despite its shortcomings, holds greatly augment existing human efforts, which otherwise overwhelmed high patient numbers.

Язык: Английский

Процитировано

130