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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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2
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Ashique S, Mishra N, Mohanto S, Garg A, Taghizadeh-Hesary F, Gowda BJ, Chellappan DK. Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects. Heliyon 2024; 10:e25754. [PMID: 38370192 PMCID: PMC10869876 DOI: 10.1016/j.heliyon.2024.e25754] [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: 08/12/2023] [Revised: 01/25/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.
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Affiliation(s)
- Sumel Ashique
- Department of Pharmaceutical Sciences, Bengal College of Pharmaceutical Sciences & Research, Durgapur, 713212, West Bengal, India
| | - Neeraj Mishra
- Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University, Gwalior, 474005, Madhya Pradesh, India
| | - Sourav Mohanto
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
| | - Ashish Garg
- Guru Ramdas Khalsa Institute of Science and Technology, Pharmacy, Jabalpur, M.P, 483001, India
| | - Farzad Taghizadeh-Hesary
- ENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Clinical Oncology Department, Iran University of Medical Sciences, Tehran, Iran
| | - B.H. Jaswanth Gowda
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
- School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, BT9 7BL, UK
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur, 57000, Malaysia
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Albahlal BM. Emerging Technology-Driven Hybrid Models for Preventing and Monitoring Infectious Diseases: A Comprehensive Review and Conceptual Framework. Diagnostics (Basel) 2023; 13:3047. [PMID: 37835793 PMCID: PMC10572974 DOI: 10.3390/diagnostics13193047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/15/2023] Open
Abstract
The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue.
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Affiliation(s)
- Bader M Albahlal
- College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia
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4
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Al-Nabulsi J, Turab N, Owida HA, Al-Naami B, De Fazio R, Visconti P. IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic. SENSORS (BASEL, SWITZERLAND) 2023; 23:7193. [PMID: 37631730 PMCID: PMC10458933 DOI: 10.3390/s23167193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.
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Affiliation(s)
- Jamal Al-Nabulsi
- Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan; (J.A.-N.); (H.A.O.)
| | - Nidal Turab
- Department of Networks and Cyber Security, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan;
| | - Hamza Abu Owida
- Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan; (J.A.-N.); (H.A.O.)
| | - Bassam Al-Naami
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan;
| | - Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy;
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Das G, Li S, Tunio RA, Jamali RH, Ullah I, Fernando KWTM. The implementation of green supply chain management (GSCM) and environmental management system (EMS) practices and its impact on market competitiveness during COVID-19. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:68387-68402. [PMID: 37121948 PMCID: PMC10149637 DOI: 10.1007/s11356-023-27077-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/13/2023] [Indexed: 05/04/2023]
Abstract
Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.
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Affiliation(s)
- Ghansham Das
- Business School, Sichuan University, Yihuan Road, Chengdu, 610065 People’s Republic of China
| | - Shan Li
- Business School, Sichuan University, Yihuan Road, Chengdu, 610065 People’s Republic of China
| | - Raza Ali Tunio
- College of Management, Sichuan Agricultural University, 211 Huimin Road, Chengdu, 611130 People’s Republic of China
| | - Riaz Hussain Jamali
- School of Economics, Sichuan University, Yihuan Road, Chengdu, 610065 People’s Republic of China
| | - Ihsan Ullah
- College of International Relations, Sichuan University, Yihuan Road, Chengdu, 610065 People’s Republic of China
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [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: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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Lyu H, Imtiaz A, Zhao Y, Luo J. Human behavior in the time of COVID-19: Learning from big data. Front Big Data 2023; 6:1099182. [PMID: 37091459 PMCID: PMC10118015 DOI: 10.3389/fdata.2023.1099182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/21/2023] [Indexed: 04/09/2023] Open
Abstract
Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups-using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.
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Affiliation(s)
| | | | | | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, United States
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8
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Mohammedqasem R, Mohammedqasim H, Asad Ali Biabani S, Ata O, Alomary MN, Almehmadi M, Amer Alsairi A, Azam Ansari M. Multi-objective deep learning framework for COVID-19 dataset problems. JOURNAL OF KING SAUD UNIVERSITY. SCIENCE 2023; 35:102527. [PMID: 36590237 PMCID: PMC9795799 DOI: 10.1016/j.jksus.2022.102527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 12/08/2022] [Accepted: 12/23/2022] [Indexed: 05/28/2023]
Abstract
Background It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.
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Affiliation(s)
- Roa'a Mohammedqasem
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Hayder Mohammedqasim
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Sardar Asad Ali Biabani
- Science and Technology Unit, Umm Al- Qura University, Makkah, Saudi Arabia & Deanship of Scientific Research, Umm Al- Qura University, Makkah, Saudi Arabia
| | - Oguz Ata
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Mohammad N Alomary
- National Centre for Biotechnology, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Mazen Almehmadi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahad Amer Alsairi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research & Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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A Statistical Synopsis of COVID-19 Components and Descriptive Analysis of Their Socio-Economic and Healthcare Aspects in Bangladesh Perspective. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2023; 2023:9738094. [PMID: 36815185 PMCID: PMC9940984 DOI: 10.1155/2023/9738094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/02/2023] [Accepted: 01/24/2023] [Indexed: 02/16/2023]
Abstract
The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z- scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.
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Liu Y, Wang X, Song C, Chen J, Shu H, Wu M, Guo S, Huang Q, Pei T. Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China. SUSTAINABLE CITIES AND SOCIETY 2023; 89:104314. [PMID: 36438675 PMCID: PMC9676079 DOI: 10.1016/j.scs.2022.104314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/18/2022] [Accepted: 11/19/2022] [Indexed: 05/27/2023]
Abstract
Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.
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Affiliation(s)
- Yaxi Liu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ci Song
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Chen
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Hua Shu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sihui Guo
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiang Huang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Pei
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Chen X, Wei X, Zhang Y, Sun Y, Xu Z, Zhang S, Li L. Reflections on major epidemics in history reported by online English news media and literature: interaction between epidemics and social conditions. Front Public Health 2023; 11:1160756. [PMID: 37113173 PMCID: PMC10126324 DOI: 10.3389/fpubh.2023.1160756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023] Open
Affiliation(s)
- Xiaorui Chen
- Foreign Languages Teaching Department, School of International Studies, Binzhou Medical University, Yantai, China
| | - Xinyu Wei
- The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China
| | - Yuanfeng Zhang
- The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China
| | - Ying Sun
- The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China
| | - Zihan Xu
- The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China
| | - Shuyuan Zhang
- The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China
| | - Lin Li
- Medicine and Pharmacy Research Center, Binzhou Medical University, Yantai, China
- *Correspondence: Lin Li
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Sheikh K, Sayeed S, Asif A, Siddiqui MF, Rafeeq MM, Sahu A, Ahmad S. Consequential Innovations in Nature-Inspired Intelligent Computing Techniques for Biomarkers and Potential Therapeutics Identification. NATURE-INSPIRED INTELLIGENT COMPUTING TECHNIQUES IN BIOINFORMATICS 2023:247-274. [DOI: 10.1007/978-981-19-6379-7_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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13
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Siddiqui MF, Alam A, Kalmatov R, Mouna A, Villela R, Mitalipova A, Mrad YN, Rahat SAA, Magarde BK, Muhammad W, Sherbaevna SR, Tashmatova N, Islamovna UG, Abuassi MA, Parween Z. Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective. NATURE-INSPIRED INTELLIGENT COMPUTING TECHNIQUES IN BIOINFORMATICS 2023:19-42. [DOI: 10.1007/978-981-19-6379-7_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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14
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Kas’janenko KV, Kozlov KV, Zhdanov KV, Lapikov II, Belikov VV. SARS-CoV-2 severity prediction in young adults using artificial intelligence. JOURNAL INFECTOLOGY 2022. [DOI: 10.22625/2072-6732-2022-14-5-14-25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Aim: to build a predictive model for severe COVID-19 prediction in young adults using deep learning methods. Materials and methods: data from 906 medical records of patients aged 18 to 44 years with laboratory-confirmed SARS-CoV-2 infection during 2020–2021 period was analyzed. Evaluation of laboratory and instrumental data was carried out using the Mann-Whitney U-test. The level of statistical significance was p≤0,05. The neural network was trained using the Pytorch framework. Results: in patients with mild to moderate SARS-CoV-2 infection, peripheral oxygen saturation, erythrocytes, hemoglobin, total protein, albumin, hematocrit, serum iron, transferrin, and absolute peripheral blood eosinophil and lymphocyte counts were significantly higher than in patients with severe СOVID-19 (p< 0,001). The values of the absolute number of neutrophils, ESR, glucose, ALT, AST, CPK, urea, LDH, ferritin, CRP, fibrinogen, D-dimer, respiration rate, heart rate, blood pressure in the group of patients with mild and moderate severity were statistically significantly lower than in the group of severe patients (p < 0.001). Eleven indicators were identified as predictors of severe COVID-19 (peripheral oxygen level, peripheral blood erythrocyte count, hemoglobin level, absolute eosinophil count, absolute lymphocyte count, absolute neutrophil count, LDH, ferritin, C-reactive protein, D-dimer levels) and their threshold values. A model intended to predict COVID-19 severity in young adults was built. Conclusion. The values of laboratory and instrumental indicators obtained in patients with SARS-CoV-2 infection upon admission significantly differ. Among them eleven indicators were significantly associated with the development of a severe COVID-19. A predictive model based on artificial intelligence method with high accuracy predicts the likelihood of severe SARS-CoV-2 course development in young adults.
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Affiliation(s)
| | - K. V. Kozlov
- Military Medical Academy named after S. M. Kirov
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15
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Kaur S, Kumar Y, Koul A, Kumar Kamboj S. A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:1863-1895. [PMID: 36465712 PMCID: PMC9702927 DOI: 10.1007/s11831-022-09853-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.
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Affiliation(s)
- Sukhpreet Kaur
- Department of Computer Science and Engineering, CGC Landran, Mohali, India
| | - Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, India
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16
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Towards data-driven models for diverging emerging technologies for maternal, neonatal and child health services in Sub-Saharan Africa: a systematic review. GLOBAL HEALTH JOURNAL 2022. [DOI: 10.1016/j.glohj.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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17
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Wang Z, Liu XF, Du Z, Wang L, Wu Y, Holme P, Lachmann M, Lin H, Wong ZS, Xu XK, Sun Y. Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model. iScience 2022; 25:105079. [PMID: 36093379 PMCID: PMC9441477 DOI: 10.1016/j.isci.2022.105079] [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: 03/12/2022] [Revised: 07/04/2022] [Accepted: 08/31/2022] [Indexed: 11/26/2022] Open
Abstract
Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation.
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Affiliation(s)
- Zhizheng Wang
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Ye Wu
- Computational Communication Research Center and School of Journalism and Communication, Beijing Normal University, Beijing, China
| | - Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
| | | | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Zoie S.Y. Wong
- Graduate School of Public Health, St. Luke’s International University, Tokyo, Japan
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Liaoning, China
| | - Yuanyuan Sun
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
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18
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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19
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Agbehadji IE, Abayomi A, Bui KHN, Millham RC, Freeman E. Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin. SENSORS (BASEL, SWITZERLAND) 2022; 22:6176. [PMID: 36015936 PMCID: PMC9415888 DOI: 10.3390/s22166176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate sorting of solid waste at the point of collection and the attendant impact on waste management process is the motivation for this study. The South African University of Technology (SAUoT) is used as a case study because solid waste management is an aspect where SAUoT is exerting an impact by leveraging emerging technologies. In this article, a convolutional neural network (CNN) based model called You-Only-Look-Once (YOLO) is employed as the object detection algorithm to facilitate the classification of waste according to various categories at the point of waste collection. Additionally, a nature-inspired search method is used as learning rate for the CNN model. The custom YOLO model was developed for waste object detection, trained with different weights and backbones, namely darknet53.conv.74, darknet19_448.conv.23, Yolov4.conv.137 and Yolov4-tiny.conv.29, respectively, for Yolov3, Yolov3-tiny, Yolov4 and Yolov4-tiny models. Eight (8) classes of waste and a total of 3171 waste images are used. The performance of YOLO models is considered in terms of accuracy of prediction (Average Precision-AP) and speed of prediction measured in milliseconds. A lower loss value out of a percentage shows a higher performance of prediction and a lower value on speed of prediction. The results of the experiment show that Yolov3 has better accuracy of prediction as compared with Yolov3-tiny, Yolov4 and Yolov4-tiny. Although the Yolov3-tiny is quick at predicting waste objects, the accuracy of its prediction is limited. The mean AP (%) for each trained version of YOLO models is Yolov3 (80%), Yolov4-tiny (74%), Yolov3-tiny (57%) and Yolov4 (41%). This result of mAP (%) indicates that the Yolov3 model produces the best performance results (80%). In this regard, it is useful to implement a model that ensures accurate prediction to develop a smart waste bin system at the institution. The experimental results show the combination of KSA learning rate parameter of 0.0007 and Yolov3 is identified as the accurate model for waste object detection and classification. The use of nature-inspired search methods, such as the Kestrel-based Search Algorithm (KSA), has shown future prospect in terms of learning rate parameter determination in waste object detection and classification. Consequently, it is imperative for an EdgeIoT-enabled system to be equipped with Yolov3 for waste object detection and classification, thereby facilitating effective waste collection.
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Affiliation(s)
- Israel Edem Agbehadji
- Honorary Research Associate, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
| | - Abdultaofeek Abayomi
- Department of Information and Communication Technology, Mangosuthu University of Technology, P.O. Box 12363, Durban 4026, South Africa
| | - Khac-Hoai Nam Bui
- Supercomputing Application Center, Korea Institute of Science and Technology Information (KISTI), Daejeon 34141, Korea
| | - Richard C. Millham
- ICT and Society Research Group, Department of Information Technology, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
| | - Emmanuel Freeman
- Department of Computer Science, Ghana Communication Technology University, Accra PMB 100, Ghana
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20
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Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. SARS-CoV-2: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022; 135:1792-1802. [PMID: 36195992 PMCID: PMC9521771 DOI: 10.1097/cm9.0000000000002058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Indexed: 02/04/2023] Open
Abstract
ABSTRACT Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.
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Affiliation(s)
- Mir Ibrahim Sajid
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Shaheer Ahmed
- Medlcal College, Islamabad Medical and Dental College, Main Murree Road, Islamabad, Pakistan
| | - Usama Waqar
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Javeria Tariq
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | | | - Samira Shabbir Balouch
- Oral and Maxillofacial Surgery, King Edward Medical University, Neela Gumbad, Lahore, Pakistan
| | - Sajid Abaidullah
- King Edward Medical University, Neela Gumbad, Lahore, Pakistan
- North Medical Ward, Mayo Hospital, Neela Gumbad, Lahore, Pakistan
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21
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Zhang Q, Gao B, Luqman A. Linking green supply chain management practices with competitiveness during covid 19: The role of big data analytics. TECHNOLOGY IN SOCIETY 2022; 70:102021. [PMID: 36090699 PMCID: PMC9439874 DOI: 10.1016/j.techsoc.2022.102021] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/06/2022] [Accepted: 06/09/2022] [Indexed: 06/10/2023]
Abstract
Although the global green supply chain management (GSCM) practice has attracted considerable scholarly attention, its efficacy for environmental management systems (EMS) and market competitiveness during Covid-19 has not been fully capitalized. Therefore, the existing literature indicates that the important link between GSCM, EMS, and market competitiveness is missing as supply management is crucial to maintaining market competitiveness. To fill this research gap, the current study examines whether EMS affects the relationship between GSCM practices and market competitiveness. We also propose the moderating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility on these associations from a Covid-19 perspective. We tested a proposed model using the primary data (N = 283) from regression-based structural equation modeling (SEM). The results provide empirical support for the impact of GSCM on ESM and market competitiveness. Furthermore, the results show that BDA-AI and environmental visibility strengthen the positive relationship between GSCM-EMS and EMS and market competitiveness, respectively. Current research provides thoughtful insights for supply chain practitioners, policymakers, managers, and academics that organizations should opt for formal EMS, BDA-AI, and environmental visibility to achieve market competitiveness, even in times of crisis such as Covid-19.
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Affiliation(s)
- Qingyu Zhang
- Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Nanshan District, Shenzhen, 518060, Guandong, China
| | - Bohong Gao
- Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Nanshan District, Shenzhen, 518060, Guandong, China
| | - Adeel Luqman
- Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Nanshan District, Shenzhen, 518060, Guandong, China
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22
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Sajid MI, Ahmed S, Waqar U, Tariq J, Chundrigarh M, Balouch SS, Abaidullah S. Application in medicine: Has artificial intelligence stood the test of time. Chin Med J (Engl) 2022; Publish Ahead of Print:00029330-990000000-00090. [PMID: 35899989 DOI: 10.1097/cm9.00000000000020s8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Indexed: 11/26/2022] Open
Abstract
ABSTRACT Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.
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Affiliation(s)
- Mir Ibrahim Sajid
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Shaheer Ahmed
- Medlcal College, Islamabad Medical and Dental College, Main Murree Road, Islamabad, Pakistan
| | - Usama Waqar
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | - Javeria Tariq
- Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan
| | | | - Samira Shabbir Balouch
- Oral and Maxillofacial Surgery, King Edward Medical University, Neela Gumbad, Lahore, Pakistan
| | - Sajid Abaidullah
- King Edward Medical University, Neela Gumbad, Lahore, Pakistan
- North Medical Ward, Mayo Hospital, Neela Gumbad, Lahore, Pakistan
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23
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Towards the Use of Big Data in Healthcare: A Literature Review. Healthcare (Basel) 2022; 10:healthcare10071232. [PMID: 35885759 PMCID: PMC9322051 DOI: 10.3390/healthcare10071232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 12/13/2022] Open
Abstract
The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.
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Giner-Galvañ V, Pomares-Gómez FJ, Quesada JA, Rubio-Rivas M, Tejada-Montes J, Baltasar-Corral J, Taboada-Martínez ML, Sánchez-Mesa B, Arnalich-Fernández F, Del Corral-Beamonte E, López-Sampalo A, Pesqueira-Fontán PM, Fernández-Garcés M, Gómez-Huelgas R, Ramos-Rincón JM. C-Reactive Protein and Serum Albumin Ratio: A Feasible Prognostic Marker in Hospitalized Patients with COVID-19. Biomedicines 2022; 10:1393. [PMID: 35740416 PMCID: PMC9219981 DOI: 10.3390/biomedicines10061393] [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: 04/25/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Background: C-reactive protein (CRP) and albumin are inflammatory markers. We analyzed the prognostic capacity of serum albumin (SA) and CRP for an outcome comprising mortality, length of stay, ICU admission, and non-invasive mechanical ventilation in hospitalized COVID-19 patients. (2) Methods: We conducted a retrospective cohort study based on the Spanish national SEMI-COVID-19 Registry. Two multivariate logistic models were adjusted for SA, CRP, and their combination. Training and testing samples were used to validate the models. (3) Results: The outcome was present in 41.1% of the 3471 participants, who had lower SA (mean [SD], 3.5 [0.6] g/dL vs. 3.8 [0.5] g/dL; p < 0.001) and higher CRP (108.9 [96.5] mg/L vs. 70.6 [70.3] mg/L; p < 0.001). In the adjusted multivariate model, both were associated with poorer evolution: SA, OR 0.674 (95% CI, 0.551−0.826; p < 0.001); CRP, OR 1.002 (95% CI, 1.001−1.004; p = 0.003). The CRP/SA model had a similar predictive capacity (honest AUC, 0.8135 [0.7865−0.8405]), with a continuously increasing risk and cutoff value of 25 showing the highest predictive capacity (OR, 1.470; 95% CI, 1.188−1.819; p < 0.001). (4) Conclusions: SA and CRP are good independent predictors of patients hospitalized with COVID-19. For the CRP/SA ratio value, 25 is the cutoff for poor clinical course.
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Affiliation(s)
- Vicente Giner-Galvañ
- Department of Internal Medicine, Hospital Clínico Universitario San Juan de Alicante, 03550 Alicante, Spain
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica (FISABIO), 46020 Valencia, Spain;
- Departamento de Medicina Clínica, Medicine School, University Miguel Hernández, 03550 Alicante, Spain; (J.A.Q.); (J.M.R.-R.)
| | - Francisco José Pomares-Gómez
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica (FISABIO), 46020 Valencia, Spain;
- Departamento de Medicina Clínica, Medicine School, University Miguel Hernández, 03550 Alicante, Spain; (J.A.Q.); (J.M.R.-R.)
- Department of Endocrinology, Hospital Clínico Universitario San Juan de Alicante, 03550 Alicante, Spain
| | - José Antonio Quesada
- Departamento de Medicina Clínica, Medicine School, University Miguel Hernández, 03550 Alicante, Spain; (J.A.Q.); (J.M.R.-R.)
| | - Manuel Rubio-Rivas
- Department of Internal Medicine, Bellvitge University Hospital, 08097 L’Hospitalet de Llobregat, Spain;
| | - Javier Tejada-Montes
- Department of Internal Medicine, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain;
| | | | | | - Blanca Sánchez-Mesa
- Department of Internal Medicine, Hospital Costa del Sol, 20603 Marbella, Spain;
| | | | | | - Almudena López-Sampalo
- Department of Internal Medicine, Regional University Hospital of Málaga, 29010 Málaga, Spain; (A.L.-S.); (R.G.-H.)
- Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), 29590 Málaga, Spain
| | - Paula María Pesqueira-Fontán
- Department of Internal Medicine, Complejo Hospitalario Universitario de Santiago, 15706 Santiago de Compostela, Spain;
| | - Mar Fernández-Garcés
- Department of Internal Medicine, Doctor Peset University Hospital, 46017 Valencia, Spain;
| | - Ricardo Gómez-Huelgas
- Department of Internal Medicine, Regional University Hospital of Málaga, 29010 Málaga, Spain; (A.L.-S.); (R.G.-H.)
- Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), 29590 Málaga, Spain
| | - José Manuel Ramos-Rincón
- Departamento de Medicina Clínica, Medicine School, University Miguel Hernández, 03550 Alicante, Spain; (J.A.Q.); (J.M.R.-R.)
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25
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Mei Y, Guo X, Chen Z, Chen Y. An Effective Mechanism for the Early Detection and Containment of Healthcare Worker Infections in the Setting of the COVID-19 Pandemic: A Systematic Review and Meta-Synthesis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105943. [PMID: 35627479 PMCID: PMC9141359 DOI: 10.3390/ijerph19105943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/09/2022] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic has exposed healthcare workers (HCWs) to serious infection risks. In this context, the proactive monitoring of HCWs is the first step toward reducing intrahospital transmissions and safeguarding the HCW population, as well as reflecting the preparedness and response of the healthcare system. As such, this study systematically reviewed the literature on evidence-based effective monitoring measures for HCWs during the COVID-19 pandemic. This was followed by a meta-synthesis to compile the key findings, thus, providing a clearer overall understanding of the subject. Effective monitoring measures of syndromic surveillance, testing, contact tracing, and exposure management are distilled and further integrated to create a whole-process monitoring workflow framework. Taken together, a mechanism for the early detection and containment of HCW infections is, thus, constituted, providing a composite set of practical recommendations to healthcare facility leadership and policy makers to reduce nosocomial transmission rates while maintaining adequate staff for medical services. In this regard, our study paves the way for future studies aimed at strengthening surveillance capacities and upgrading public health system resilience, in order to respond more efficiently to future pandemic threats.
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Affiliation(s)
- Yueli Mei
- School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China; (Y.M.); (X.G.); (Z.C.)
- Shanghai Jiao Tong University-Yale University Joint Center for Health Policy, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiuyun Guo
- School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China; (Y.M.); (X.G.); (Z.C.)
| | - Zhihao Chen
- School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China; (Y.M.); (X.G.); (Z.C.)
| | - Yingzhi Chen
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
- Correspondence: ; Tel.: +86-135-649-90786
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Saheb T, Sabour E, Qanbary F, Saheb T. Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies. TECHNOLOGY IN SOCIETY 2022; 69:101968. [PMID: 35342210 PMCID: PMC8934188 DOI: 10.1016/j.techsoc.2022.101968] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/13/2022] [Accepted: 03/18/2022] [Indexed: 05/02/2023]
Abstract
As the COVID-19 pandemic expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of the COVID Tracing Application (CTA) was one of these measures. In this study, we employed bibliometric and topic-based content analysis to determine the most significant entities and research topics. Additionally, we identified significant privacy concerns posed by CTAs, which gather, store, and analyze data in partnership with large technology corporations using proximity measurement technologies, artificial intelligence, and blockchain. We examined a series of key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, techno governance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the privacy of mobile health applications that are embedded with cutting-edge technologies and are employed during global pandemics.
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Affiliation(s)
- Tahereh Saheb
- Tarbiat Modares University, Management Studies Center, Tarbiat Modares University, Jalal Al Ahmad, Tehran, Iran
| | - Elham Sabour
- Tarbiat Modares University, Information Technology Management- Business Intelligence, Iran
| | - Fatimah Qanbary
- Tarbiat Modares University, Information Technology Management- Business Intelligence, Iran
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Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Speech as a Biomarker for COVID-19 Detection Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6093613. [PMID: 35444694 PMCID: PMC9014833 DOI: 10.1155/2022/6093613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 11/30/2022]
Abstract
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
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Ilbeigipour S, Albadvi A. Symptom-based analysis of COVID-19 cases using supervised machine learning approaches to improve medical decision-making. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100933. [PMID: 35434262 PMCID: PMC9004256 DOI: 10.1016/j.imu.2022.100933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/26/2022] [Accepted: 03/26/2022] [Indexed: 12/13/2022] Open
Abstract
The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.
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Affiliation(s)
- Sadegh Ilbeigipour
- Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
| | - Amir Albadvi
- Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
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Anand S, Sharma V, Pourush R, Jaiswal S. A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19. Ann Med Surg (Lond) 2022; 76:103519. [PMID: 35401978 PMCID: PMC8975609 DOI: 10.1016/j.amsu.2022.103519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used 'web of Science', 'Scopus' and 'goggle scholar' with the keywords of "SARS-CoV-2 detection" or "coronavirus 2019 detection" or "COVID 2019 detection" or "COVID 19 detection" "corona virus techniques for detection of COVID-19", "audio techniques for detection of COVID-19", "speech techniques for detection of COVID-19", for period of 2019-2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single "perfect" COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
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Affiliation(s)
- Satyajit Anand
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Vikrant Sharma
- Mechanical Engineering, Mody University of Science and Technology, India
| | - Rajeev Pourush
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Sandeep Jaiswal
- Biomedical Engineering, Mody University of Science and Technology, India
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Broza YY, Haick H. Biodiagnostics in an era of global pandemics-From biosensing materials to data management. VIEW 2022; 3:20200164. [PMID: 34766159 PMCID: PMC8441813 DOI: 10.1002/viw.20200164] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 03/10/2021] [Accepted: 04/12/2021] [Indexed: 12/15/2022] Open
Abstract
The novel corona virus SARS-CoV-2 (COVID-19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini-review provides and examines evidence-based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini-review provides a prospective on the requirements to improve our coping of the pandemic-related biodiagnostics as well as future opportunities.
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Affiliation(s)
- Yoav Y. Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifaIsrael
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifaIsrael
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32
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Amen B, Faiz S, Do TT. Big data directed acyclic graph model for real-time COVID-19 twitter stream detection. PATTERN RECOGNITION 2022; 123:108404. [PMID: 34744186 PMCID: PMC8556703 DOI: 10.1016/j.patcog.2021.108404] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 09/15/2021] [Accepted: 10/24/2021] [Indexed: 05/06/2023]
Abstract
Every day, large-scale data are continuously generated on social media as streams, such as Twitter, which inform us about all events around the world in real-time. Notably, Twitter is one of the effective platforms to update countries leaders and scientists during the coronavirus (COVID-19) pandemic. Other people have also used this platform to post their concerns about the spread of this virus and a rapid increase of death cases globally. The aim of this work is to detect anomalous events associated with COVID-19 from Twitter. To this end, we propose a distributed Directed Acyclic Graph topology framework to aggregate and process large-scale real-time tweets related to COVID-19. The core of our system is a novel lightweight algorithm that can automatically detect anomaly events. In addition, our system can also identify, cluster, and visualize important keywords in tweets. On 18 August 2020, our model detected the highest anomaly since many tweets mentioned the casualties' updates and the debates on the pandemic that day. We obtained the three most commonly listed terms on Twitter: "covid", "death", and "Trump" (21,566, 11,779, and 4761 occurrences, respectively), with the highest TF-IDF score for these terms: "people" (0.63637), "school" (0.5921407) and "virus" (0.57385). From our clustering result, the word "death", "corona", and "case" are grouped into one cluster, where the word "pandemic", "school", and "president" are grouped as another cluster. These terms were located near each other on vector space so that they were clustered, indicating people's most concerned topics on Twitter.
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Affiliation(s)
- Bakhtiar Amen
- Department of Computer Science, School of Electrical Engineering, Electronics, and Computer Science, University of Liverpool, Liverpool L69 3BX, UK
| | - Syahirul Faiz
- State Islamic Institute of Surakarta (IAIN Surakarta), Indonesia
| | - Thanh-Toan Do
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Australia
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Abstract
To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic; instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer” algorithm, providing an infection risk assessment for individuals in potential contact with patients.
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34
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An evaluation of critical knowledge areas for managing the COVID-19 pandemic. JOURNAL OF KNOWLEDGE MANAGEMENT 2022. [DOI: 10.1108/jkm-01-2021-0083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Purpose
The ability to manage the COVID-19 pandemic is contingent upon the ability to effectively manage its heterogeneous knowledge resources. Knowledge mapping represents a great opportunity to create value by bringing stakeholders together, facilitating comprehensive collaboration and facilitating broader in-depth knowledge sharing and transfer. However, identifying and analysing critical knowledge areas is one of the most important steps when creating a knowledge map. Therefore, the purpose of this paper is to appraise the critical knowledge areas for managing COVID-19, and thereby enhance decision-making in tackling the consequences of the pandemic.
Design/methodology/approach
The methodological approach for this study is a critical literature review, covering publications on knowledge management, knowledge mapping and COVID-19. EBSCOhost, PubMed, Scopus, Science Direct, TRID, Web of Science and Wiley Online Library were searched for full text, peer-reviewed articles written in English that investigated on critical knowledge areas for managing the spread of COVID-19. After full screening, 21 articles met the criteria for inclusion and were analysed and reported.
Findings
The study revealed seven critical knowledge areas for managing the COVID-19 pandemic. These are cleaning and disinfection; training, education and communication; reporting guidance and updates; testing; infection control measures, personal protective equipment; and potential COVID-19 transmission in health and other care settings. The study developed a concept knowledge map illustrating areas of critical knowledge which decision-makers need to be aware of.
Practical implications
Providing decision-makers with access to key knowledge during the COVID-19 pandemic seems to be crucial for effective decision-making. This study has provided insights for the professionals and decision-makers identifying the critical knowledge areas for managing the COVID-19 pandemic.
Social implications
The study advances the literature on knowledge management and builds a theoretical link with the management of public health emergencies. Additionally, the findings support the theoretical position that knowledge maps facilitate decision-making and help users to identify critical knowledge areas easily and effectively.
Originality/value
This study fills gaps in the existing literature by providing an explicit representation of know-how for managing the COVID-19 pandemic. This paper uses an objective and qualitative approach by reviewing related publications, reports and guidelines in the analysis. The concept map illustrates the critical knowledge areas for managing the COVID-19 pandemic.
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35
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Abdeldayem OM, Dabbish AM, Habashy MM, Mostafa MK, Elhefnawy M, Amin L, Al-Sakkari EG, Ragab A, Rene ER. Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:149834. [PMID: 34525746 PMCID: PMC8379898 DOI: 10.1016/j.scitotenv.2021.149834] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/05/2021] [Accepted: 08/18/2021] [Indexed: 05/06/2023]
Abstract
A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.
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Affiliation(s)
- Omar M Abdeldayem
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands.
| | - Areeg M Dabbish
- Biotechnology Graduate Program, Biology Department, School of Science and Engineering, The American University in Cairo, New Cairo 11835, Egypt
| | - Mahmoud M Habashy
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands
| | - Mohamed K Mostafa
- Faculty of Engineering and Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt
| | - Mohamed Elhefnawy
- CanmetENERGY, 1615 Lionel-Boulet Blvd, P.O. Box 4800, Varennes, Québec J3X 1P7, Canada; Department of Mathematics and Industrial Engineering, Polytechnique Montréal 2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada
| | - Lobna Amin
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands; Department of Built Environment, Aalto University, PO Box 15200, FI-00076, Aalto, Finland
| | - Eslam G Al-Sakkari
- Chemical Engineering Department, Cairo University, Cairo University Road, 12613 Giza, Egypt
| | - Ahmed Ragab
- CanmetENERGY, 1615 Lionel-Boulet Blvd, P.O. Box 4800, Varennes, Québec J3X 1P7, Canada; Department of Mathematics and Industrial Engineering, Polytechnique Montréal 2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada; Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands
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Xia Y, Wang X, Wu W, Shi H. Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8414135. [PMID: 35035861 PMCID: PMC8759879 DOI: 10.1155/2022/8414135] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 10/20/2021] [Indexed: 11/18/2022]
Abstract
The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance (P < 0.05). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance (P < 0.05). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.
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Affiliation(s)
- Yanmei Xia
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Xiuzhe Wang
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Weidong Wu
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
| | - Haipeng Shi
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan 030032, China
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Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning. NOVEL AI AND DATA SCIENCE ADVANCEMENTS FOR SUSTAINABILITY IN THE ERA OF COVID-19 2022. [PMCID: PMC9069062 DOI: 10.1016/b978-0-323-90054-6.00007-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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38
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Alo UR, Nkwo FO, Nweke HF, Achi II, Okemiri HA. Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions. SENSORS (BASEL, SWITZERLAND) 2021; 22:280. [PMID: 35009822 PMCID: PMC8749862 DOI: 10.3390/s22010280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.
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Affiliation(s)
- Uzoma Rita Alo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Friday Onwe Nkwo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Friday Nweke
- Centre for Research in Machine Learning, Artificial Intelligence and Network Systems, Computer Science Department, Ebonyi State University, P.M.B 053, Abakaliki 480211, Ebonyi State, Nigeria;
| | - Ifeanyi Isaiah Achi
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Anayo Okemiri
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
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39
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Qiu H, Du W. Evaluation of the Effect of PDCA in Hospital Health Management. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6778045. [PMID: 34966526 PMCID: PMC8712153 DOI: 10.1155/2021/6778045] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 12/11/2022]
Abstract
PDCA plays a very important role in the hygienic management of hospital operating rooms. Before the PDCA management from February 2018 to February 2019, routine hygiene management in the operating room was adopted; after the PDCA management from February 2018 to February 2019, PDCA cycle management was adopted. 500 surgical patients were randomly selected from both groups. We compare the quality control checklist scores of hand hygiene management, environmental hygiene management, medical waste management, and disinfection and isolation management in the routine group and the PDCA group. We also compared the detection rate of pathogenic bacteria in surgical incisions of the routine group and the PDCA group and the infection rate of various surgical incisions, air samples, surface samples, disinfectants, and sterilization rates of sterile items. The PDCA group's hand hygiene management score, environmental hygiene management score, medical waste management score, and disinfection and isolation management score were higher than those in the conventional group (P < 0.05). The detection rate of pathogenic bacteria in the PDCA group, the infection rate of type I, type II, and type III incisions, and the total infection rate of surgical incisions were lower than those in the conventional group, and the difference was statistically significant (P < 0.05). The qualified rate of air samples and surface samples in the PDCA group was higher than that in the conventional group (P < 0.05), and the difference in the qualified rate of disinfectants and sterile items between the two groups was not statistically significant (P > 0.05). The hygiene management of the operating room adopts PDCA sustainable improvement management measures to effectively increase the qualification rate of disinfection and sterilization, reduce the detection rate of pathogenic bacteria and infection rate of surgical incisions, and strengthen the management quality of hand hygiene, environmental hygiene, medical waste, and disinfection and isolation.
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Affiliation(s)
- Huanmin Qiu
- Party Committee Office, The First Affiliated Hospital of Suchow University, Suzhou 215006, China
| | - Weiwei Du
- Propaganda Department of Party Committee, Renmin Hospital of Wuhan University, Wuhan 430060, China
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Guo L. Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2961697. [PMID: 34956565 PMCID: PMC8702308 DOI: 10.1155/2021/2961697] [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: 09/17/2021] [Revised: 10/22/2021] [Accepted: 10/29/2021] [Indexed: 11/17/2022]
Abstract
This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised learning model and stack self-coding network. The ultrasonic images of 1,725 patients with breast lesions in the shared database are used as the test data of the model. The differences in accuracy (Acc), recall (RE), sensitivity (Sen), and running time between the two models before and after optimization and other algorithms are compared. A total of 48 female patients with nipple discharge are enrolled. The differences in SE, specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of conventional ultrasound and contrast-enhanced ultrasonography are analyzed based on pathological examination results. The results showed that when the number of network layers is 5, the classification accuracies of DNS and ODNS model data reached the highest values, which were 91.45% and 98.64%, respectively.
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Affiliation(s)
- Ling Guo
- Pingxiang People's Hospital, Pingxiang 337000, China
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41
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Banerjee B, Jani A, Shah N. Digital Image Encryption Using Double Crossover Approach for SARS-CoV-2 Infected Lungs in a Blockchain Framework. FRONTIERS IN BLOCKCHAIN 2021. [DOI: 10.3389/fbloc.2021.771241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
As the (Covid-19) pandemic spreads, the creativity of the scientific community is thriving while trying to control the situation. They are trying to treat patients viably and work with the almost exhausted medical equipment and staff, while growing new, successful antibodies. Successful screening of SARS-CoV-2 empowers fast and proficient determination of COVID-19 and can relieve the weight on medical care frameworks. Numerous forecast models are being created to comprehend and prognosticate the spread of the pandemic and to stay away from the following wave. But in the coming time, we can be sure that the models would experience the ill effects of a few issues, security being one of them. All the models need to be built in such a way that the investigation task gets successfully conducted without compromising the privacy and security of the patients. To take care of this, we propose a blockchain framework for sharing patients’ personal data or medical reports. A blockchain will take care of the integrity part, but we still need to worry about confidentiality. Therefore, combining a genetic approach with a blockchain seemed like a good idea. A twofold hybrid methodology is proposed in this paper to tackle the issue of confidentiality. The outcomes displayed high entropy accomplishment for the utilized dataset. The sensitivity of the plaintext and ciphertext is also checked and compared with existing approaches which thus demonstrates the security of the proposed approach in the given setting.
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D'Agostino M, Marti M, Otero P, Doane D, Brooks I, Garcia Saiso S, Nelson J, Tejerina L, Bagolle A, Medina Mejia F, Luna D, Curioso WH, Lourenço V, Malek V, de Cosio G. Toward a holistic definition for Information Systems for Health in the age of digital interdependence. Rev Panam Salud Publica 2021; 45:e143. [PMID: 34840555 PMCID: PMC8612596 DOI: 10.26633/rpsp.2021.143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/14/2021] [Indexed: 01/19/2023] Open
Abstract
The article’s main objective is to propose a new definition for Information Systems for Health, which is characterized by the identification and involvement of all the parts of a complex and interconnected process for data collection and decision-making in public health in the information society. The development of the concept was through a seven-step process including document analysis, on-site and virtual sessions for experts, and an online survey of broader health professionals. This new definition seeks to provide a holistic view, process, and approach for managing interoperable applications and databases that ethically considers open and free access to structured and unstructured data from different sectors, strategic information, and information and communication technology (ICT) tools for decision-making for the benefit of public health. It also supports the monitoring of the Sustainable Development Goals and the implementation of universal access to health and universal health coverage as well as Health in All Policies as an approach to promote health-related policies across sectors. Information Systems for Health evolves from preconceptions of health information systems to an integrated and multistakeholder effort that ensures better care and better policy-making and decision-making.
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Affiliation(s)
- Marcelo D'Agostino
- Pan American Health Organization Washington, D.C. United States of America Pan American Health Organization, Washington, D.C., United States of America
| | - Myrna Marti
- International Consultant Argentina International Consultant, Argentina
| | - Paula Otero
- Hospital Italiano de Buenos Aires Buenos Aires Argentina Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Daniel Doane
- International Consultant Canada International Consultant, Canada
| | - Ian Brooks
- University of Illinois Champaign United States of America University of Illinois, Champaign, Ill., United States of America
| | - Sebastian Garcia Saiso
- Pan American Health Organization Washington, D.C. United States of America Pan American Health Organization, Washington, D.C., United States of America
| | - Jennifer Nelson
- Inter-American Development Bank Washington, D.C. United States of America Inter-American Development Bank, Washington, D.C., United States of America
| | - Luis Tejerina
- Inter-American Development Bank Washington, D.C. United States of America Inter-American Development Bank, Washington, D.C., United States of America
| | - Alexandre Bagolle
- Inter-American Development Bank Washington, D.C. United States of America Inter-American Development Bank, Washington, D.C., United States of America
| | | | - Daniel Luna
- Hospital Italiano de Buenos Aires Buenos Aires Argentina Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Walter H Curioso
- Universidad Continental Lima Peru Universidad Continental, Lima, Peru
| | - Viviane Lourenço
- International Consultant United States of America International Consultant, United States of America
| | - Victoria Malek
- International Consultant Argentina International Consultant, Argentina
| | - Gerardo de Cosio
- Pan American Health Organization Washington, D.C. United States of America Pan American Health Organization, Washington, D.C., United States of America
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Javaid M, Haleem A, Pratap Singh R, Suman R. Pedagogy and innovative care tenets in COVID-19 pandemic: An enhancive way through Dentistry 4.0. SENSORS INTERNATIONAL 2021; 2:100118. [PMID: 34766061 PMCID: PMC8302480 DOI: 10.1016/j.sintl.2021.100118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 12/24/2022] Open
Abstract
The global oral healthcare sector has now woken to implement Dentistry 4.0. The implementation of this revolution is feasible with extensive digital and advanced technologies applications and the adoption of new sets of processes in dentistry & its support areas. COVID-19 has bought new challenges to dental professionals and patients towards their customised requirements, regular dental health checkups, fast-paced and safe procedures. People are not visiting the dentist even for mild cases as they fear COVID-19 infection. We see that this set of technologies will help improve health education and treatment process and materials and minimise the infection. During the COVID-19 pandemic, there is a need to understand the possible impact of Dentistry 4.0 for education and innovative care. This paper discusses the significant benefits of Dentistry 4.0 technologies for the smart education platform and dentistry treatment. Finally, this article identifies twenty significant enhancements in dental education and effective care platforms during the COVID-19 pandemic by employing Dentistry 4.0 technologies. Thus, proper implementation of these technologies will improve the process efficiency in healthcare during the COVID-19 pandemic. Dentistry 4.0 technologies drive innovations to improve the quality of internet-connected healthcare devices. It creates automation and exchanges data to make a smart health care system. Therefore, helps better healthcare services, planning, monitoring, teaching, learning, treatment, and innovation capability. These technologies moved to smart transportation systems in the hospital during the COVID-19 Pandemic. Modern manufacturing technologies create digital transformation in manufacturing, optimises the operational processes and enhances productivity.
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Affiliation(s)
- Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Abid Haleem
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Ravi Pratap Singh
- Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India
| | - Rajiv Suman
- Department of Industrial & Production Engineering, G.B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India
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Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software. J Pers Med 2021; 11:jpm11111103. [PMID: 34834455 PMCID: PMC8623042 DOI: 10.3390/jpm11111103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/16/2022] Open
Abstract
Objective: To investigate two commercial software and their efficacy in the assessment of chest CT sequelae in patients affected by COVID-19 pneumonia, comparing the consistency of tools. Materials and Methods: Included in the study group were 120 COVID-19 patients (56 women and 104 men; 61 years of median age; range: 21–93 years) who underwent chest CT examinations at discharge between 5 March 2020 and 15 March 2021 and again at a follow-up time (3 months; range 30–237 days). A qualitative assessment by expert radiologists in the infectious disease field (experience of at least 5 years) was performed, and a quantitative evaluation using thoracic VCAR software (GE Healthcare, Chicago, Illinois, United States) and a pneumonia module of ANKE ASG-340 CT workstation (HTS Med & Anke, Naples, Italy) was performed. The qualitative evaluation included the presence of ground glass opacities (GGOs) consolidation, interlobular septal thickening, fibrotic-like changes (reticular pattern and/or honeycombing), bronchiectasis, air bronchogram, bronchial wall thickening, pulmonary nodules surrounded by GGOs, pleural and pericardial effusion, lymphadenopathy, and emphysema. A quantitative evaluation included the measurements of GGOs, consolidations, emphysema, residual healthy parenchyma, and total lung volumes for the right and left lung. A chi-square test and non-parametric test were utilized to verify the differences between groups. Correlation coefficients were used to analyze the correlation and variability among quantitative measurements by different computer tools. A receiver operating characteristic (ROC) analysis was performed. Results: The correlation coefficients showed great variability among the quantitative measurements by different tools when calculated on baseline CT scans and considering all patients. Instead, a good correlation (≥0.6) was obtained for the quantitative GGO, as well as the consolidation volumes obtained by two tools when calculated on baseline CT scans, considering the control group. An excellent correlation (≥0.75) was obtained for the quantitative residual healthy lung parenchyma volume, GGO, consolidation volumes obtained by two tools when calculated on follow-up CT scans, and for residual healthy lung parenchyma and GGO quantification when the percentage change of these volumes were calculated between a baseline and follow-up scan. The highest value of accuracy to identify patients with RT-PCR positive compared to the control group was obtained by a GGO total volume quantification by thoracic VCAR (accuracy = 0.75). Conclusions: Computer aided quantification could be an easy and feasible way to assess chest CT sequelae due to COVID-19 pneumonia; however, a great variability among measurements provided by different tools should be considered.
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021; 11:993. [PMID: 34683133 PMCID: PMC8540782 DOI: 10.3390/jpm11100993] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. METHODS Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). RESULTS Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). CONCLUSIONS Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
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Affiliation(s)
- Roberta Fusco
- IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy;
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
| | - Diletta Cozzi
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Biagio Pecori
- Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
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Murray S, Koksma J, Haramati A, Bonnefoy E, Zary N, Bill W, Wolkenhauer O, Price S, Kummerle D. Imagining the Future of Learning in Healthcare: The GAME 2019 #FuturistForum. J Eur CME 2021; 10:1984076. [PMID: 34621599 PMCID: PMC8491664 DOI: 10.1080/21614083.2021.1984076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 11/22/2022] Open
Abstract
The GAME 2019 #FuturistForum involved an exchange of ideas and perspectives on the future of learning in healthcare and necessary evolutions to sustain future health systems. This event allowed for reflection and discourse around a) what medical learning or learning in healthcare may look like 10-15 years from now, b) how technology would impact that evolution, and c) what collaborative roles distinct stakeholders would play. Seventy-five (75) key stakeholders, experts from various fields, participated in the two-day event. Four multifaceted themes were uncovered from the qualitative analysis, which are: learning will be lifelong and outcome-based, the health system will follow a preventive care model, technology will be an enabler of evolution in education and health systems, and that multi-level collaboration will support and sustain future progress. Future implications, exacerbated by the ongoing COVID-19 pandemic, and study limitations are described.
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Affiliation(s)
- Suzanne Murray
- GAME Board of Directors
- CEO & founder of AXDEV Group Inc, Brossard, Canada
| | - Jur Koksma
- GAME Board of Directors
- Radboud University Medical Center, Nijmegen, Netherlands
| | - Aviad Haramati
- Center for Innovation and Leadership in Education, Georgetown University Medical Center, Washington, DC, USA
| | - Eric Bonnefoy
- Service d'urgences Cardiologiques et de Soins Intensifs de Cardiologie, Hôpital Louis Pradel, Bron, France
| | - Nabil Zary
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Werner Bill
- European Respiratory Society, Lausanne, Switzerland
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Universität Rostock, Rostock, Germany
| | - Susanna Price
- Intensive Care Unit, Royal Brompton & Harefield NHS Foundation Trust, London, UK
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Chinitz LA. Atrial Arrhythmias and the Pandemic. JACC Clin Electrophysiol 2021; 7:1131-1133. [PMID: 34556286 PMCID: PMC8451505 DOI: 10.1016/j.jacep.2021.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Larry A Chinitz
- Leon H. Charney Division of Cardiology, New York University Langone Health, New York University Grossman School of Medicine, New York, New York, USA.
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COVID-19 Pandemic Waves: 4IR Technology Utilisation in Multi-Sector Economy. SUSTAINABILITY 2021. [DOI: 10.3390/su131810168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In this paper, we reviewed the Fourth Industrial Revolution (4IR) technologies applied to waves of the coronavirus disease (COVID-19). COVID-19 is an existential threat that has resulted in an unprecedented loss of lives, disruption of flight schedules, shutdown of businesses and much more. Though several researchers have highlighted the enormous benefits of 4IR technologies in containing the COVID-19 pandemic, the recent waves of the pandemic call for a thorough review of these technological interventions. The cyber-physical space has had its share of the COVID-19 pandemic effect, and through this review, we highlight the salient issues to help policy formulation towards managing the impact of subsequent COVID-19 waves within such environments. Hence, the purpose of this paper is to review the application of 4IR technologies during the COVID-19 pandemic waves and to highlight their shortcomings. Recent research articles were sourced from an online repository and thoroughly reviewed to highlight 4IR technology applications, innovations, shortcomings and multi-sector challenges. The outcome of this review indicates that the second wave of the pandemic resulted in a lower proportion of patients requiring invasive mechanical ventilation and a lower rate of thrombotic events. In addition, it was revealed that the delay between ICU admissions and tracheal intubation was longer in the second wave in the health care sector. Again, the review suggests that 4IR technologies have been utilized across all the sectors including education, businesses, society, manufacturing, healthcare, agriculture and mining. Businesses have revised their service delivery models to include 4IR technologies and avoid physical contacts. In society, digital certificates, among other digital platforms, have been utilized to assist with the movements of persons who have been vaccinated. Manufacturing concerns have also utilized robots in manufacturing to reduce human-to-human physical contact. The mining sector has automated their work processes, utilising smart boots to prevent infection, smart health bands and smart disinfection tunnels or walkthrough sanitization gates in the mining work environment. However, the identified challenges of implementing 4IR technologies include low-skilled workers, data privacy issues, data analysis poverty, data management issues and many more. The boom in 4IR technologies calls for intense legislation on sweeping data privacy for regulated tech companies. These findings hold salient implications for policy formulation towards tackling future pandemic outbreaks.
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Zhang F. Application of machine learning in CT images and X-rays of COVID-19 pneumonia. Medicine (Baltimore) 2021; 100:e26855. [PMID: 34516488 PMCID: PMC8428739 DOI: 10.1097/md.0000000000026855] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.
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