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Chauhan S, Shinde RK, Jain Y. Navigating Abdominal Volvulus: A Comprehensive Review of Management Strategies. Cureus 2024; 16:e57978. [PMID: 38738029 PMCID: PMC11086050 DOI: 10.7759/cureus.57978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024] Open
Abstract
Abdominal volvulus represents a critical condition characterized by the abnormal twisting of the GI tract, potentially leading to obstruction and vascular compromise. Prompt recognition and appropriate management are essential to prevent complications and improve patient outcomes. This comprehensive review examines the anatomy, pathophysiology, clinical presentation, and diagnostic evaluation of, and management strategies for abdominal volvulus. Non-operative techniques, including detorsion and decompression, as well as surgical interventions, such as laparoscopic and open approaches, are discussed. Additionally, the importance of multidisciplinary collaboration and postoperative care is emphasized. Despite significant advancements, unresolved issues remain, necessitating further research to refine diagnostic and therapeutic approaches. Future directions, including exploring emerging technologies, offer promise for enhancing the management of this challenging condition. Overall, this review provides clinicians with valuable insights into the optimal management of abdominal volvulus, aiming to improve patient outcomes and enhance clinical practice.
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Affiliation(s)
- Simran Chauhan
- General Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Raju K Shinde
- General Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Yashraj Jain
- General Surgery, Rajshree Nursing Home, Ashoknagar, IND
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2
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Karthick S, Gomathi N. IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm. Med Biol Eng Comput 2024; 62:925-940. [PMID: 38095786 DOI: 10.1007/s11517-023-02973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 11/15/2023] [Indexed: 02/22/2024]
Abstract
New potential for healthcare has been made possible by the development of the Internet of Medical Things (IoMT) with deep learning. This is applied for a broad range of applications. Normal medical devices together with sensors can gather important data when connected to the Internet, and deep learning uses this data to reveal symptoms and patterns and activate remote care. In recent years, the COVID-19 pandemic caused more mortality. Millions of people have been affected by this virus, and the number of infections is continually rising daily. To detect COVID-19, researchers attempt to utilize medical imaging and deep learning-based methods. Several methodologies were suggested utilizing chest X-ray (CXR) images for COVID-19 diagnosis. But these methodologies do not provide satisfactory accuracy. To overcome these drawbacks, a recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm (RERNN-GEO) is proposed in this paper. The intention of this work is to provide IoT-based deep learning method for the premature identification of COVID-19. This paradigm can be able to ease the workload of radiologists and medical specialists and also help with pandemic control. RERNN-GEO is a deep learning-based method; this is utilized in chest X-ray (CXR) images for COVID-19 diagnosis. Here, the Gray-Level Co-Occurrence Matrix (GLCM) window adaptive algorithm is used for extracting features to enable accurate diagnosis. By utilizing this algorithm, the proposed method attains better accuracy (33.84%, 28.93%, and 33.03%) and lower execution time (11.06%, 33.26%, and 23.33%) compared with the existing methods. This method can be capable of helping the clinician/radiologist to validate the initial assessment related to COVID-19.
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Affiliation(s)
- Karthick S
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi - NCR Campus, Ghaziabad, India.
| | - Gomathi N
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
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3
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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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Zhang D, Kukkar D, Kim KH, Bhatt P. A comprehensive review on immunogen and immune-response proteins of SARS-CoV-2 and their applications in prevention, diagnosis, and treatment of COVID-19. Int J Biol Macromol 2024; 259:129284. [PMID: 38211928 DOI: 10.1016/j.ijbiomac.2024.129284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
Exposure to severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) prompts humoral immune responses in the human body. As the auxiliary diagnosis of a current infection, the existence of viral proteins can be checked from specific antibodies (Abs) induced by immunogenic viral proteins. For people with a weakened immune system, Ab treatment can help neutralize viral antigens to resist and treat the disease. On the other hand, highly immunogenic viral proteins can serve as effective markers for detecting prior infections. Additionally, the identification of viral particles or the presence of antibodies may help establish an immune defense against the virus. These immunogenic proteins rather than SARS-CoV-2 can be given to uninfected people as a vaccination to improve their coping ability against COVID-19 through the generation of memory plasma cells. In this work, we review immunogenic and immune-response proteins derived from SARS-CoV-2 with regard to their classification, origin, and diverse applications (e.g., prevention (vaccine development), diagnostic testing, and treatment (via neutralizing Abs)). Finally, advanced immunization strategies against COVID-19 are discussed along with the contemporary circumstances and future challenges.
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Affiliation(s)
- Daohong Zhang
- College of Food Engineering, Ludong University, Yantai 264025, Shandong, China; Bio-Nanotechnology Research Institute, Ludong University, Yantai 264025, Shandong, China
| | - Deepak Kukkar
- Department of Biotechnology, Chandigarh University, Gharuan, Mohali 140413, Punjab, India; University Center for Research and Development, Chandigarh University, Gharuan, Mohali 140413, Punjab, India
| | - Ki-Hyun Kim
- Department of Civil & Environmental Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul 04763, Republic of Korea.
| | - Poornima Bhatt
- Department of Biotechnology, Chandigarh University, Gharuan, Mohali 140413, Punjab, India; University Center for Research and Development, Chandigarh University, Gharuan, Mohali 140413, Punjab, India
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Molleví Bortoló G, Álvarez Valdés J, Nicolas-Sans R. Sustainable, technological, and innovative challenges post Covid-19 in health, economy, and education sectors. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023; 190:122424. [PMID: 36811031 PMCID: PMC9933853 DOI: 10.1016/j.techfore.2023.122424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/11/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
The impact of the Covid-19 pandemic has caused an evolution in the business use of Big Data, Artificial Intelligence and New Technologies in general. The general objective of the article is to assess how this process developed during the pandemic in the use and standardization of Big Data, digitalization, the use of data in the private sector and in the public administration and to assess whether it has been used to modernize and digitalize the post-pandemic society. The specific objectives of the article are: 1) the impact of new technologies on society during confinement; 2) to understand the use of Big Data for the creation of new products and businesses and 3) to assess which businesses and companies and from which economic sectors have emerged, which have been transformed and which have disappeared.
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Affiliation(s)
- Gemma Molleví Bortoló
- ESIC Business & Marketing School, ESIC University, Passeig Santa Eulàlia, 2. 08017 Barcelona, Spain
| | - Jesús Álvarez Valdés
- ESIC Business & Marketing School, ESIC University, Passeig Santa Eulàlia, 2. 08017 Barcelona, Spain
| | - Ruben Nicolas-Sans
- ESIC Business & Marketing School, ESIC University, Passeig Santa Eulàlia, 2. 08017 Barcelona, Spain
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Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (SCIENCE) 2023; 28:323-329. [PMID: 36846270 PMCID: PMC9942022 DOI: 10.1007/s12204-023-2581-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/28/2022] [Indexed: 02/23/2023]
Abstract
This study focuses on a robot vision localization method for coping with the operational task of automatic nasal swab sampling. The application is important in the detection and epidemic prevention of Corona Virus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumonia owing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the strong infectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition. The visual navigation and positioning method using a single-arm robot for sampling is also planned, which considers the operation characteristics of medical staff. In the decision network, the risk factor for potential contact infection caused by swab sampling operations is established to avoid the spread among personnel. A robot visual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swab sampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning for the robots and provide technical support for managing new major public health situations.
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7
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Ozsahin DU, Isa NA, Uzun B. The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis. Diagnostics (Basel) 2022; 12:diagnostics12122943. [PMID: 36552949 PMCID: PMC9777320 DOI: 10.3390/diagnostics12122943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Sciences, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Correspondence: (D.U.O.); (N.A.I.)
| | - Nuhu Abdulhaqq Isa
- Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Biomedical Engineering, College of Health Science and Technology, Keffi 961101, Keffi Nasarawa State, Nigeria
- Correspondence: (D.U.O.); (N.A.I.)
| | - Berna Uzun
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Statistics, Carlos III Madrid University, 28903 Getafe, Madrid, Spain
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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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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9
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review. Int J Prev Med 2022; 13:112. [PMID: 36247189 PMCID: PMC9564228 DOI: 10.4103/ijpvm.ijpvm_667_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 09/21/2021] [Indexed: 01/08/2023] Open
Abstract
Background The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak. Methods This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model. Results In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at "on-body," "in-clinic/hospital," and even "in-community" levels. Conclusions The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran,Address for correspondence: Dr. Hadi Kazemi-Arpanahi, Assistant professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. E-mail:
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Tarannum S, Jabin S. Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY 2022; 14:2469-2475. [PMID: 35669982 PMCID: PMC9152827 DOI: 10.1007/s41870-022-00971-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/23/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Shahla Tarannum
- Department of Computer Science, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Suraiya Jabin
- Department of Computer Science, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
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11
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Teleconsultation adoption since COVID-19: comparison of barriers and facilitators in primary care settings in Hong Kong and the Netherlands. Health Policy 2022; 126:933-944. [PMID: 36050194 PMCID: PMC9356914 DOI: 10.1016/j.healthpol.2022.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/22/2022]
Abstract
The COVID-19 pandemic has boosted the adoption of digital health technologies such as teleconsultation. This research aimed to assess and compare barriers and facilitators for teleconsultation uptake for primary care practitioners in Hong Kong and the Netherlands and evaluate the role of their different healthcare funding models in this adoption process within the context of the COVID-19 pandemic. A qualitative research following a social constructivist paradigm was performed. The study employed a conceptual framework from Lau and colleagues that identifies four levels of factors influencing change in primary care: (1) external contextual factors; (2) organization-related factors; (3) professional factors; and (4) characteristics of the intervention. The four levels were studied through semi-structured, open-ended interviews with primary care physicians. External factors were additionally assessed by means of a literature review. Hong Kong and the Netherlands showed different penetration rates of teleconsultation. Most stakeholders in both settings shared similar barriers and facilitators in the organizational, professional, and intervention levels. However, external contextual factors (i.e., current teleconsultation legislation, available incentives, and level of public awareness) played an important and differing role in teleconsultation uptake and had a direct effect on the organization, the professionals involved, and the type of technology used. Political and organizational actions are required to develop a comprehensive legal framework for the sustainable development of teleconsultation in both settings.
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Gianquintieri L, Brovelli MA, Pagliosa A, Dassi G, Brambilla PM, Bonora R, Sechi GM, Caiani EG. Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9012. [PMID: 35897382 PMCID: PMC9330211 DOI: 10.3390/ijerph19159012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.
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Affiliation(s)
- Lorenzo Gianquintieri
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
| | - Maria Antonia Brovelli
- Civil and Environmental Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
| | - Andrea Pagliosa
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Gabriele Dassi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Piero Maria Brambilla
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Rodolfo Bonora
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Giuseppe Maria Sechi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Enrico Gianluca Caiani
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
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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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14
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Subramanian M, Shanmuga Vadivel K, Hatamleh WA, Alnuaim AA, Abdelhady M, V E S. The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis. EXPERT SYSTEMS 2022; 39:e12834. [PMID: 34898797 PMCID: PMC8646626 DOI: 10.1111/exsy.12834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 05/17/2023]
Abstract
Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.
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Affiliation(s)
- Malliga Subramanian
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
| | | | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services King Saud University Riyadh Saudi Arabia
| | - Mohamed Abdelhady
- Electrical and Computer Engineering Department Cleveland State University Cleveland Ohio USA
| | - Sathishkumar V E
- Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu India
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15
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Hao F, Zheng K. Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6736249. [PMID: 35449857 PMCID: PMC9018189 DOI: 10.1155/2022/6736249] [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: 02/02/2022] [Revised: 03/02/2022] [Accepted: 03/12/2022] [Indexed: 11/18/2022]
Abstract
The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recommendation system. The system integrates machine learning algorithms and knowledge graph technology to help patients conduct online consultations. The system analyses the misclassification data of different departments through high-frequency word analysis. The study found that the accuracy rate of our machine learning algorithm model to identify entities in electronic medical records reached 96.29%. This type of model can effectively screen out the most important pathogenic features.
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Affiliation(s)
- Feng Hao
- Jilin Medical University, Jilin 132013, China
| | - Kai Zheng
- Jilin Medical University, Jilin 132013, China
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16
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Alzahrani AI, Al-Samarraie H, Eldenfria A, Dodoo JE, Alalwan N. Users' intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic. TECHNOLOGY IN SOCIETY 2022; 68:101862. [PMID: 35013631 PMCID: PMC8730779 DOI: 10.1016/j.techsoc.2022.101862] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 12/24/2021] [Accepted: 01/01/2022] [Indexed: 05/30/2023]
Abstract
The coronavirus disease 2019 (COVID-19) has changed the way we use and perceive online services. This study examined the influence of service quality factors during COVID-19 on individuals' intention to continue use mHealth services. A decision-making trial and evaluation laboratory (DEMATEL) approach was used to identify and analyse the relationships between service quality and individuals' intention to continue use mHealth during the COVID-19 pandemic. Individuals' direct, indirect, and interdependent behaviours in relation to service quality and continues use of mHealth were studied. A total of 126 respondents were involved in this study. The results identified several associations between service quality factors and individuals' continuous use of mHealth. The most important factor found to influence users' decision to continuously use mHealth was assurance, followed by hedonic benefits, efficiency, reliability, and content quality. The relevant cause-and-effect relationships were identified and the direction for quality improvement was discussed. The outcomes from this study can support healthcare policy makers to swiftly and widely respond to COVID-19 challenges. The findings provide fundamental insights for healthcare organisations to promote continuous use of mHealth among people by prioritising service improvements.
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Affiliation(s)
| | | | - Atef Eldenfria
- Faculty of Information Technology, Misurata University, Misurata, Libya
| | - Joana Eva Dodoo
- College of Distance Education, Department of Business Studies, University of Cape Coast, Cape Coast, Ghana
| | - Nasser Alalwan
- Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
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17
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Basir MT, Abbas SR. Applications of digital and smart technologies to control SARS-CoV-2 transmission, rapid diagnosis, and monitoring. BIOTECHNOLOGY IN HEALTHCARE 2022:259-271. [DOI: 10.1016/b978-0-323-90042-3.25001-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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18
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Yaacoub JPA, Noura HN, Salman O, Chehab A. Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations. INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2022; 21:115-158. [PMID: 33776611 PMCID: PMC7978470 DOI: 10.1007/s10207-021-00545-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.
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Affiliation(s)
- Jean-Paul A. Yaacoub
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, 1107 2020 Lebanon
| | - Hassan N. Noura
- FEMTO-ST Institute, Univ. Bourgogne Franche-Comté (UBFC), Besançon, France
| | - Ola Salman
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, 1107 2020 Lebanon
| | - Ali Chehab
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, 1107 2020 Lebanon
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19
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Rupesh Kumar J, Mayandi K, Joe Patrick Gnanaraj S, Chandrasekar K, Sethu Ramalingam P. A critical review of an additive manufacturing role in Covid-19 epidemic. MATERIALS TODAY. PROCEEDINGS 2022; 68:1521-1527. [PMID: 35874090 PMCID: PMC9289093 DOI: 10.1016/j.matpr.2022.07.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In 2019, a massive and deadly coronavirus pandemic known as the COVID-19 pandemic has swept through more than 180 nations, causing a massive strain on already overtaxed health systems around the globe. Global demand for medical equipment has put a strain on traditional manufacturing methods, resulting in the need for an efficient, low-cost, and speedy mode of production. Additive manufacturing, or 3D printing, has been used by manufacturers to bridge the gap and enhance the production of medical products. Some designs that had been previously or conventionally fabricated have been revised to meet the 3D printing requirement for combating COVID-19. A variety of designs were created, and they are now in use in hospitals by patients and healthcare professionals. However, because some gadgets must adhere to rigorous standards, it is possible that some items will not meet these requirements. As a result, in order to protect the health of the user, it is necessary to understand each gadget, its usage, and industry standards. An investigation of the usage of additive manufacturing during the COVID-19 epidemic is presented in this paper. It brings together the manufacturers of a variety of 3D-printed products, including face shields, face masks, valves, nasopharyngeal swabs, and others, to debate their application and regulatory concerns in the medical field. The primary shortcoming of technology, discussed in reference to the next pandemic, is addressed here. It also looks at some of the ways that additive manufacturing could be used in the future during an emergency.
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Affiliation(s)
- Jinka Rupesh Kumar
- Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
| | - K Mayandi
- Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
| | - S Joe Patrick Gnanaraj
- Department of Mechanical Engineering, St.Mother Theresa Engineering College, Tamilnadu, India
| | - K Chandrasekar
- Department of Mechanical Engineering, PSN College of Engineering and Technology, Tirunelveli, TamilNadu, India
| | - P Sethu Ramalingam
- Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India
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20
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Hafke-Dys H, Kuźnar-Kamińska B, Grzywalski T, Maciaszek A, Szarzyński K, Kociński J. Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients. Front Physiol 2021; 12:745635. [PMID: 34858203 PMCID: PMC8632553 DOI: 10.3389/fphys.2021.745635] [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: 07/22/2021] [Accepted: 10/18/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person. Aim: We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation. Methods: The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups. Results: Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA. Conclusions: The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.
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Affiliation(s)
- Honorata Hafke-Dys
- Department of Acoustics, Faculty of Physics, Adam Mickiewicz University in Poznań, Poznań, Poland.,StethoMe Sp. z o.o., Poznań, Poland
| | - Barbara Kuźnar-Kamińska
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Poznań, Poland
| | | | | | | | - Jędrzej Kociński
- Department of Acoustics, Faculty of Physics, Adam Mickiewicz University in Poznań, Poznań, Poland.,StethoMe Sp. z o.o., Poznań, Poland
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21
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Diaz JV, Herridge M, Bertagnolio S, Davis HE, Dua T, Kaushic C, Marshall JC, del Rosario Pérez M, Strub-Wourgaft N, Soriano JB. Towards a universal understanding of post COVID-19 condition. Bull World Health Organ 2021; 99:901-903. [PMID: 34866686 PMCID: PMC8640686 DOI: 10.2471/blt.21.286249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/07/2021] [Accepted: 06/24/2021] [Indexed: 12/28/2022] Open
Affiliation(s)
- Janet V Diaz
- Clinical Management Team, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland
| | - Margaret Herridge
- Department of Medicine, Interdepartmental Division of Critical Care, University of Toronto, Toronto, Canada
| | - Silvia Bertagnolio
- Department of Control, Surveillance and Prevention of Antimicrobial Resistance, World Health Organization, Geneva, Switzerland
| | - Hannah E Davis
- Patient-Led Research Collaborative, United States of America
| | - Tarun Dua
- Brain Health Unit, World Health Organization, Geneva, Switzerland
| | - Charu Kaushic
- CIHR Institute of Infection and Immunity, McMaster University, Hamilton, Canada
| | - John C Marshall
- Departments of Surgery and Critical Care Medicine, St Michael's Hospital, Toronto, Canada
| | - Maria del Rosario Pérez
- Clinical Management Team, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland
| | | | - Joan B Soriano
- Clinical Management Team, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland
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22
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Ahmed I, Ahmad A, Jeon G. An IoT-Based Deep Learning Framework for Early Assessment of Covid-19. IEEE INTERNET OF THINGS JOURNAL 2021; 8:15855-15862. [PMID: 35782174 PMCID: PMC8768983 DOI: 10.1109/jiot.2020.3034074] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/06/2020] [Accepted: 10/20/2020] [Indexed: 05/03/2023]
Abstract
Advancement in the Internet of Medical Things (IoMT), along with machine learning, deep learning, and artificial intelligence techniques, initiated a world of possibilities in healthcare. It has an extensive range of applications: when connected to the Internet, ordinary medical devices and sensors can collect valuable data, deep learning, and artificial intelligence techniques utilize this data and give an insight of symptoms, trends and enable remote care. Recently, Covid-19 pandemic outbreak caused the death of a large number of people. This virus has infected millions of people, and still, the rate of infected people is increasing day by day. Researchers are endeavoring to utilize medical images and deep learning-based models for the detection of Covid-19. Various techniques have been presented that utilize X-Ray images of the chest for the detection of Covid-19. However, the importance of regional-based convolutional neural networks (CNNs) is currently confined. Thus, this research aimed to introduce an IoT-based deep learning framework for early assessment of Covid-19. This framework can reduce the working pressure of medical experts/radiologists and contribute to the pandemic control. A deep learning-based model, i.e., faster regions with CNNs (Faster-RCNN) with ResNet-101, is applied on X-Ray images of the chest for Covid-19 detection. It uses region proposal network (RPN) to perform detection. By employing the model, we achieve a detection accuracy of 98%. Therefore, we believe that the system might be capable in order to assist medical expert/radiologist, to verify early assessment toward Covid-19.
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Affiliation(s)
- Imran Ahmed
- Center of Excellence in Information TechnologyInstitute of Management Sciences Peshawar 25000 Pakistan
| | - Awais Ahmad
- Department of Computer ScienceAir University Islamabad 44000 Pakistan
| | - Gwanggil Jeon
- School of Electronic EngineeringXidian University Xi'an 710071 China
- Department of Embedded Systems EngineeringIncheon National University Incheon 22012 South Korea
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23
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Schardt JS, Pornnoppadol G, Desai AA, Park KS, Zupancic JM, Makowski EK, Smith MD, Chen H, Garcia de Mattos Barbosa M, Cascalho M, Lanigan TM, Moon JJ, Tessier PM. Discovery and characterization of high-affinity, potent SARS-CoV-2 neutralizing antibodies via single B cell screening. Sci Rep 2021; 11:20738. [PMID: 34671080 PMCID: PMC8528929 DOI: 10.1038/s41598-021-99401-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/22/2021] [Indexed: 12/17/2022] Open
Abstract
Monoclonal antibodies that target SARS-CoV-2 with high affinity are valuable for a wide range of biomedical applications involving novel coronavirus disease (COVID-19) diagnosis, treatment, and prophylactic intervention. Strategies for the rapid and reliable isolation of these antibodies, especially potent neutralizing antibodies, are critical toward improved COVID-19 response and informed future response to emergent infectious diseases. In this study, single B cell screening was used to interrogate antibody repertoires of immunized mice and isolate antigen-specific IgG1+ memory B cells. Using these methods, high-affinity, potent neutralizing antibodies were identified that target the receptor-binding domain of SARS-CoV-2. Further engineering of the identified molecules to increase valency resulted in enhanced neutralizing activity. Mechanistic investigation revealed that these antibodies compete with ACE2 for binding to the receptor-binding domain of SARS-CoV-2. These antibodies may warrant further development for urgent COVID-19 applications. Overall, these results highlight the potential of single B cell screening for the rapid and reliable identification of high-affinity, potent neutralizing antibodies for infectious disease applications.
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Affiliation(s)
- John S. Schardt
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Ghasidit Pornnoppadol
- grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Alec A. Desai
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Kyung Soo Park
- grid.214458.e0000000086837370Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Jennifer M. Zupancic
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Emily K. Makowski
- grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Matthew D. Smith
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Hongwei Chen
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | | | - Marilia Cascalho
- grid.214458.e0000000086837370Department of Surgery, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109 USA
| | - Thomas M. Lanigan
- grid.214458.e0000000086837370Division of Rheumatology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - James J. Moon
- grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Peter M. Tessier
- grid.214458.e0000000086837370Departments of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370University of Michigan, North Campus Research Complex, B10-179, 2800 Plymouth Road, Ann Arbor, MI 48109 USA
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24
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Rehman M, Shah RA, Khan MB, Shah SA, AbuAli NA, Yang X, Alomainy A, Imran MA, Abbasi QH. Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset. SENSORS (BASEL, SWITZERLAND) 2021; 21:6750. [PMID: 34695963 PMCID: PMC8538545 DOI: 10.3390/s21206750] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/30/2021] [Accepted: 10/08/2021] [Indexed: 12/27/2022]
Abstract
The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20-30% of COVID patients require hospitalization, while almost 5-12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne-Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.
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Affiliation(s)
- Mubashir Rehman
- Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan; (M.R.); (R.A.S.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan;
| | - Raza Ali Shah
- Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan; (M.R.); (R.A.S.)
| | - Muhammad Bilal Khan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan;
| | - Syed Aziz Shah
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK;
| | - Najah Abed AbuAli
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates;
| | - Xiaodong Yang
- School of Electronic Engineering, Xidian University, Xi’an 710071, China;
| | - Akram Alomainy
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK;
| | - Muhmmad Ali Imran
- School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
- Artificial Intelligence Research Centre (AIRC), Ajman University, Ajman 20550, United Arab Emirates
| | - Qammer H. Abbasi
- School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
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25
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Russo G, Di Salvatore V, Caraci F, Curreli C, Viceconti M, Pappalardo F. How can we accelerate COVID-19 vaccine discovery? Expert Opin Drug Discov 2021; 16:1081-1084. [PMID: 34058925 PMCID: PMC8204312 DOI: 10.1080/17460441.2021.1935861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/25/2021] [Indexed: 12/24/2022]
Affiliation(s)
- Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Oasi Research Institute, IRCCS, Troina, Italy
| | - Valentina Di Salvatore
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Filippo Caraci
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Oasi Research Institute, IRCCS, Troina, Italy
| | - Cristina Curreli
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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26
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Quiroz-Juárez MA, Torres-Gómez A, Hoyo-Ulloa I, León-Montiel RDJ, U’Ren AB. Identification of high-risk COVID-19 patients using machine learning. PLoS One 2021; 16:e0257234. [PMID: 34543294 PMCID: PMC8452016 DOI: 10.1371/journal.pone.0257234] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 08/26/2021] [Indexed: 12/21/2022] Open
Abstract
The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.
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Affiliation(s)
- Mario A. Quiroz-Juárez
- Departamento de Física, Universidad Autónoma Metropolitana Unidad Iztapalapa, Ciudad de México, México
- * E-mail:
| | | | | | | | - Alfred B. U’Ren
- Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México, México
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27
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Li Q, Zhan X, Wang J, Lu H. Considerations and guidance to control the rebound in COVID-19 cases. Biosci Trends 2021; 15:341-344. [PMID: 34456212 DOI: 10.5582/bst.2021.01361] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, the COVID-19 pandemic has caused a serious crisis for healthcare systems worldwide. COVID-19 vaccine coverage has increased in many countries, but the COVID-19 epidemic has rapidly expanded, with a daily increase of 30,390 COVID-19 cases and 9,761 deaths since August 12, 2021. This article provides a brief overview of growing concerns about a rebound of the COVID-19 pandemic caused by the Delta variant and public health epidemic control measures that have recently been relaxed. As of August 13, 2021, 465,679 cases of COVID-19 due to the Delta variant of SARS-CoV-2 have been detected in over 120 countries. Epidemic control measures were relaxed in some areas, such as allowing large gatherings and improper criteria for ending self-isolation. Even in China, where the epidemic was tightly controlled with strict non-pharmaceutical interventions (NPIs), new COVID-19 cases, and asymptomatic cases in particular, spiked in the first 13 days of August. More importantly, most of those cases were local, while most of the cases accounting for the previous increase were imported. Therefore, relaxed epidemic control measures and asymptomatic infections possibly caused by the Delta variant of SARS-CoV-2 may increase the risk of virus transmission. Accordingly, suggestions for COVID-19 containment, such as encouraging vaccination of the general population, using Internet of Things technology (loT) to reduce the possibility of contact with the asymptomatic infected, and enhancing disease surveillance, have been offered here.
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Affiliation(s)
- Qian Li
- Department of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China.,Department of Infectious Diseases, Shanghai Public Health Clinical Center, Shanghai, China
| | - Xiangming Zhan
- Department of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China.,Department of Infectious Diseases, Shanghai Public Health Clinical Center, Shanghai, China
| | - Jun Wang
- Clinical Laboratory, The Fifth People's Hospital of Wuxi, Jiangnan University, Wuxi, Jiangnan, China
| | - Hongzhou Lu
- Department of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, Guangdong, China.,Department of Infectious Diseases, Shanghai Public Health Clinical Center, Shanghai, China
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Bae YS, Kim KH, Choi SW, Ko T, Lim JS, Piao M. Satisfaction and Usability of an Information and Communications Technology-Based System by Clinically Healthy Patients With COVID-19 and Medical Professionals: Cross-sectional Survey and Focus Group Interview Study. JMIR Form Res 2021; 5:e26227. [PMID: 34254946 PMCID: PMC8396536 DOI: 10.2196/26227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/22/2020] [Accepted: 07/06/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Digital health care is an important strategy in the war against COVID-19. South Korea introduced living and treatment support centers (LTSCs) to control regional outbreaks and care for patients with asymptomatic or mild COVID-19. Seoul National University Hospital (SNUH) introduced information and communications technology (ICT)-based solutions to manage clinically healthy patients with COVID-19. OBJECTIVE This study aims to investigate satisfaction and usability by patients and health professionals in the optimal use of a mobile app and wearable device that SNUH introduced to the LTSC for clinically healthy patients with COVID-19. METHODS Online surveys and focus group interviews were conducted to collect quantitative and qualitative data. RESULTS Regarding usability testing of the wearable device, perceived usefulness had the highest mean score of 4.45 (SD 0.57) points out of 5. Regarding usability of the mobile app, perceived usefulness had the highest mean score of 4.62 (SD 0.48) points out of 5. Regarding satisfaction items for the mobile app among medical professionals, the "self-reporting" item had the highest mean score of 4.42 (SD 0.58) points out of 5. In focus group interviews of health care professionals, hospital information system interfacing was the most important functional requirement for ICT-based COVID-19 telemedicine. CONCLUSIONS Improvement of patient safety and reduction of the burden on medical staff were the expected positive outcomes. Stability and reliability of the device, patient education, accountability, and reimbursement issues should be considered as part of the development of remote patient monitoring. In responding to a novel contagious disease, telemedicine and a wearable device were shown to be useful during a global crisis.
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Affiliation(s)
- Ye Seul Bae
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kyung Hwan Kim
- Department of Thoracic & Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Thoracic & Cardiovascular Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sae Won Choi
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea
| | - Taehoon Ko
- Department of Medical Informatics, Catholic University of Korea, Seoul, Republic of Korea
| | - Jun Seo Lim
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea
| | - Meihua Piao
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea
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Yayehrad AT, Siraj EA, Wondie GB, Alemie AA, Derseh MT, Ambaye AS. Could Nanotechnology Help to End the Fight Against COVID-19? Review of Current Findings, Challenges and Future Perspectives. Int J Nanomedicine 2021; 16:5713-5743. [PMID: 34465991 PMCID: PMC8402990 DOI: 10.2147/ijn.s327334] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 07/29/2021] [Indexed: 12/21/2022] Open
Abstract
A serious viral infectious disease was introduced to the globe by the end of 2019 that was seen primarily from China, but spread worldwide in a few months to be a pandemic. Since then, accurate prevention, early detection, and effective treatment strategies are not yet outlined. There is no approved drug to counter its worldwide transmission. However, integration of nanostructured delivery systems with the current management strategies has promised a pronounced opportunity to tackle the pandemic. This review addressed the various promising nanotechnology-based approaches for the diagnosis, prevention, and treatment of the pandemic. The pharmaceutical, pharmacoeconomic, and regulatory aspects of these systems with currently achieved or predicted beneficial outcomes, challenges, and future perspectives are also highlighted.
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Affiliation(s)
- Ashagrachew Tewabe Yayehrad
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
- Department of Pharmacy, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Ebrahim Abdela Siraj
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
- Department of Pharmacy, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Gebremariam Birhanu Wondie
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Atlaw Abate Alemie
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
- Ethiopian Food and Drug Authority (EFDA), Federal Ministry of Health (FMoH), Addis Ababa, Ethiopia
| | - Manaye Tamrie Derseh
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
- Departement of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan-Aman, Ethiopia
| | - Abyou Seyfu Ambaye
- Departement of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, Mizan-Tepi University, Mizan-Aman, Ethiopia
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Rodríguez-Rodríguez I, Rodríguez JV, Shirvanizadeh N, Ortiz A, Pardo-Quiles DJ. Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8578. [PMID: 34444327 PMCID: PMC8393243 DOI: 10.3390/ijerph18168578] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/02/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.
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Affiliation(s)
- Ignacio Rodríguez-Rodríguez
- Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden;
| | - José-Víctor Rodríguez
- Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;
| | - Niloofar Shirvanizadeh
- Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden;
| | - Andrés Ortiz
- Departamento de Ingeniería de Comunicaciones, School of Telecommunications Engineering, Universidad de Málaga, 29071 Málaga, Spain;
| | - Domingo-Javier Pardo-Quiles
- Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;
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Wang XV, Wang L. A literature survey of the robotic technologies during the COVID-19 pandemic. JOURNAL OF MANUFACTURING SYSTEMS 2021; 60:823-836. [PMID: 33612914 PMCID: PMC7881735 DOI: 10.1016/j.jmsy.2021.02.005] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 05/06/2023]
Abstract
Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies' perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies' perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work's technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.
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Affiliation(s)
- Xi Vincent Wang
- Department of Production Engineering, KTH Royal Institute of Technology, Sweden
| | - Lihui Wang
- Department of Production Engineering, KTH Royal Institute of Technology, Sweden
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Wang Y, Ahmed A, Azam A, Bing D, Shan Z, Zhang Z, Tariq MK, Sultana J, Mushtaq RT, Mehboob A, Xiaohu C, Rehman M. Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing. JOURNAL OF MANUFACTURING SYSTEMS 2021; 60:709-733. [PMID: 35068653 PMCID: PMC8759146 DOI: 10.1016/j.jmsy.2021.07.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/17/2021] [Accepted: 07/17/2021] [Indexed: 05/09/2023]
Abstract
Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies. Three-dimensional printing (3DP) reduces global energy consumption and CO2 emissions related to industrial manufacturing. Various renewable energy harvesting mechanisms utilizing solar, wind, tidal and human potential have been fabricated through additive manufacturing. 3D printing aided the manufacturing companies in combating the deficiencies of medical healthcare devices for patients and professionals globally. In this regard, 3D printed medical face shields, respiratory masks, personal protective equipment, PLA-based recyclable air filtration masks, additively manufactured ideal tissue models and new information technology (IT) based rapid manufacturing are some significant contributions of 3DP. Furthermore, a bibliometric study of 3D printing research was conducted in CiteSpace. The most influential keywords and latest research frontiers were found and the 3DP knowledge was categorized into 10 diverse research themes. The potential challenges incurred by AM industry during the pandemic were categorized in terms of design, safety, manufacturing, certification and legal issues. Significantly, this study highlights the versatile role of 3DP in battle against COVID-19 pandemic and provides up-to-date research frontiers, leading the readers to focus on the current hurdles encountered by AM industry, henceforth conduct further investigations to enhance 3DP technology.
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Affiliation(s)
- Yanen Wang
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Ammar Ahmed
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Ali Azam
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, PR China
| | - Du Bing
- Center of Stomatology, The Second People's Hospital of Foshan, Foshan, 528000, PR China
| | - Zhang Shan
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Zutao Zhang
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, PR China
| | - Muhammad Kashif Tariq
- Department of Mechanical Engineering, University of Engineering & Technology, Lahore, 54890, Pakistan
| | - Jakiya Sultana
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Ray Tahir Mushtaq
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Asad Mehboob
- Department of Material Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Chen Xiaohu
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Mudassar Rehman
- Department of Industry Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
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Tareq MS, Rahman T, Hossain M, Dorrington P. Additive manufacturing and the COVID-19 challenges: An in-depth study. JOURNAL OF MANUFACTURING SYSTEMS 2021; 60:787-798. [PMID: 33897085 PMCID: PMC8058390 DOI: 10.1016/j.jmsy.2020.12.021] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/28/2020] [Accepted: 12/30/2020] [Indexed: 05/09/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status. The pandemic created huge demand for relevant medical and personal protective equipment (PPE) and put unprecedented pressure on the healthcare system within a very short span of time. Moreover, the supply chain system faced extreme disruption as a result of the frequent and severe lockdowns across the globe. In such a situation, additive manufacturing (AM) becomes a supplementary manufacturing process to meet the explosive demands and to ease the health disaster worldwide. Providing the extensive design customization, a rapid manufacturing route, eliminating lengthy assembly lines and ensuring low manufacturing lead times, the AM route could plug the immediate supply chain gap, whilst mass production routes restarted again. The AM community joined the fight against COVID-19 by producing components for medical equipment such as ventilators, nasopharyngeal swabs and PPE such as face masks and face shields. The aim of this article is to systematically summarize and to critically analyze all major efforts put forward by the AM industry, academics, researchers, users, and individuals. A step-by-step account is given summarizing all major additively manufactured products that were designed, invented, used, and produced during the pandemic in addition to highlighting some of the potential challenges. Such a review will become a historical document for the future as well as a stimulus for the next generation AM community.
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Affiliation(s)
- Md Sarower Tareq
- Department of Mechanical Engineering, Michigan State University, East Lansing, USA
| | - Tanzilur Rahman
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, USA
| | - Mokarram Hossain
- Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, SA1 8EN, United Kingdom
| | - Peter Dorrington
- College of Engineering, Swansea University, SA1 8EN, United Kingdom
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Rahman MM, Ahmed M, Islam MT, Khan MR, Sultana S, Maeesa SK, Hasan S, Hossain MA, Ferdous KS, Mathew B, Rauf A, Uddin MS. Nanotechnology-Based Approaches and Investigational Therapeutics against COVID-19. Curr Pharm Des 2021; 28:948-968. [PMID: 34218774 DOI: 10.2174/1381612827666210701150315] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 04/30/2021] [Indexed: 01/08/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus which is currently responsible for the global pandemic since December 2019. This class of coronavirus has affected 217 countries around the world. Most of the countries have taken some non-remedial preventive actions like country lockdown, work from home, travel bans, and the most significant one is social isolation. Pharmacists, doctors, nurses, technologists, and all other healthcare professionals are playing a pivotal role during this pandemic. Unluckily, there is no specific drug that can treat patients who are confirmed with COVID-19, though favipiravir and remdesivir have appeared as favorable antiviral drugs. Some vaccines have already developed, and vaccination has started worldwide. Different nanotechnologies are in the developing stage in many countries for preventing SARS-COV-2 and treating COVID-19 conditions. In this article, we review the COVID-19 pandemic situation as well as the nanotechnology-based approaches and investigational therapeutics against COVID-19.
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Affiliation(s)
- Md Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Muniruddin Ahmed
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Mohammad Touhidul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Md Robin Khan
- Bangladesh Reference Institute for Chemical Measurements, Dhaka. Bangladesh
| | - Sharifa Sultana
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Saila Kabir Maeesa
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Sakib Hasan
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Md Abid Hossain
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Kazi Sayma Ferdous
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka. Bangladesh
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi 682041, India
| | - Abdur Rauf
- Department of Chemistry, University of Swabi, Swabi, Anbar, Khyber Pakhtunkhwa. Bangladesh
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka. Bangladesh
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Donida B, da Costa CA, Scherer JN. Making the COVID-19 Pandemic a Driver for Digital Health: Brazilian Strategies. JMIR Public Health Surveill 2021; 7:e28643. [PMID: 34101613 PMCID: PMC8244723 DOI: 10.2196/28643] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
The COVID-19 outbreak exposed several problems faced by health systems worldwide, especially concerning the safe and rapid generation and sharing of health data. However, this pandemic scenario has also facilitated the rapid implementation and monitoring of technologies in the health field. In view of the occurrence of the public emergency caused by SARS-CoV-2 in Brazil, the Department of Informatics of the Brazilian Unified Health System created a contingency plan. In this paper, we aim to report the digital health strategies applied in Brazil and the first results obtained during the fight against COVID-19. Conecte SUS, a platform created to store all the health data of an individual throughout their life, is the center point of the Brazilian digital strategy. Access to the platform can be obtained through an app by the patient and the health professionals involved in the case. Health data sharing became possible due to the creation of the National Health Data Network (Rede Nacional de Dados em Saúde, RNDS). A mobile app was developed to guide citizens regarding the need to go to a health facility and to assist in disseminating official news about the virus. The mobile app can also alert the user if they have had contact with an infected person. The official numbers of cases and available hospital beds are updated and published daily on a website containing interactive graphs. These data are obtained due to creating a web-based notification system that uses the RNDS to share information about the cases. Preclinical care through telemedicine has become essential to prevent overload in health facilities. The exchange of experiences between medical teams from large centers and small hospitals was made possible using telehealth. Brazil took a giant step toward digital health adoption, creating and implementing important initiatives; however, these initiatives do not yet cover the entire health system. It is expected that the sharing of health data that are maintained and authorized by the patient will become a reality in the near future. The intention is to obtain better clinical outcomes, cost reduction, and faster and better services in the public health network.
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Affiliation(s)
- Bruna Donida
- SOFTWARELAB - Software Innovation Laboratory, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
| | - Cristiano André da Costa
- SOFTWARELAB - Software Innovation Laboratory, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
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Last K, Power NR, Dellière S, Velikov P, Šterbenc A, Antunovic IA, Lopes MJ, Schweitzer V, Barac A. Future developments in training. Clin Microbiol Infect 2021; 27:1595-1600. [PMID: 34197928 PMCID: PMC8280350 DOI: 10.1016/j.cmi.2021.06.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/16/2021] [Accepted: 06/19/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of highly skilled and extensively trained specialists in clinical microbiology (CM) and infectious diseases (ID). Training curricula in CM and ID must constantly evolve to prepare trainees for future pandemics and to allow trainees to reach their full clinical and academic potential. OBJECTIVES In this narrative review, we aim to outline necessary future adaptations in CM and ID training curricula and identify current structural barriers in training with the aim of discussing possibilities to address these shortcomings. SOURCES We reviewed literature from PubMed and included selected books and online publications as appropriate. There was no time constraint on the included publications. CONTENT Drawing from the lessons learnt during the pandemic, we summarize novel digital technologies relevant to CM and ID trainees and highlight interdisciplinary teamwork and networking skills as important competencies. We centre CM and ID training within the One Health framework and discuss gender inequalities and structural racism as barriers in both CM and ID training and patient care. IMPLICATIONS CM and ID trainees should receive training and support developing skills in novel digital technologies, leadership, interdisciplinary teamwork and networking. Equally important is the need for equity of opportunity, with firm commitments to end gender inequality and structural racism in CM and ID. Policy-makers and CM and ID societies should ensure that trainees are better equipped to achieve their professional goals and are better prepared for the challenges awaiting in their fields.
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Affiliation(s)
- Katharina Last
- Centre for Infectious Diseases, Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany.
| | - Nicholas R Power
- Royal College of Physicians of Ireland, Setanta House, 1 Setanta Pl, Dublin 2, Ireland
| | - Sarah Dellière
- Université de Paris, Laboratoire de Parasitologie-Mycologie, Groupe Hospitalier Saint-Louis-Lariboisière-Fernand-Widal, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Petar Velikov
- Infectious Diseases Hospital Prof. Ivan Kirov and Department of Infectious Diseases, Parasitology and Tropical Medicine, Medical University of Sofia, Sofia, Bulgaria
| | - Anja Šterbenc
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ivana Antal Antunovic
- Department of Clinical Microbiology, University Hospital for Infectious Diseases, Zagreb, Croatia
| | - Maria João Lopes
- Infectious Diseases Department, Hospital Professor Doutor Fernando Fonseca, Amadora, Portugal
| | - Valentijn Schweitzer
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Aleksandra Barac
- Clinic for Infectious and Tropical Diseases, University Clinical Centre of Serbia, Belgrade, Serbia
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Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021; 457:40-66. [PMID: 34149184 PMCID: PMC8206574 DOI: 10.1016/j.neucom.2021.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.
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Affiliation(s)
- Weiping Ding
- School of Information Science and Technology, Nantong University, China
| | - Janmenjoy Nayak
- Aditya Institute of Technology and Management (AITAM), India
| | - H Swapnarekha
- Aditya Institute of Technology and Management (AITAM), India
- Veer Surendra Sai University of Technology, India
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Gao A, Murphy RR, Chen W, Dagnino G, Fischer P, Gutierrez MG, Kundrat D, Nelson BJ, Shamsudhin N, Su H, Xia J, Zemmar A, Zhang D, Wang C, Yang GZ. Progress in robotics for combating infectious diseases. Sci Robot 2021; 6:6/52/eabf1462. [PMID: 34043552 DOI: 10.1126/scirobotics.abf1462] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/09/2021] [Indexed: 12/24/2022]
Abstract
The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.
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Affiliation(s)
- Anzhu Gao
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.,Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Robin R Murphy
- Humanitarian Robotics and AI Laboratory, Texas A&M University, College Station, TX, USA
| | - Weidong Chen
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.,Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Giulio Dagnino
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK.,University of Twente, Enschede, Netherlands
| | - Peer Fischer
- Institute of Physical Chemistry, University of Stuttgart, Stuttgart, Germany.,Micro, Nano, and Molecular Systems Laboratory, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | | | - Dennis Kundrat
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK
| | | | | | - Hao Su
- Biomechatronics and Intelligent Robotics Lab, Department of Mechanical Engineering, City University of New York, City College, New York, NY 10031, USA
| | - Jingen Xia
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 100029 Beijing, China.,National Center for Respiratory Medicine, 100029 Beijing, China.,Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, 100029 Beijing, China.,National Clinical Research Center for Respiratory Diseases, 100029 Beijing, China
| | - Ajmal Zemmar
- Department of Neurosurgery, Henan Provincial People's Hospital, Henan University People's Hospital, Henan University School of Medicine, 7 Weiwu Road, 450000 Zhengzhou, China.,Department of Neurosurgery, University of Louisville, School of Medicine, 200 Abraham Flexner Way, Louisville, KY 40202, USA
| | - Dandan Zhang
- Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK
| | - Chen Wang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, 100029 Beijing, China.,National Center for Respiratory Medicine, 100029 Beijing, China.,Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, 100029 Beijing, China.,National Clinical Research Center for Respiratory Diseases, 100029 Beijing, China.,Chinese Academy of Medical Sciences, Peking Union Medical College, 100730 Beijing, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, 200240 Shanghai, China.
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Dabla PK, Gruson D, Gouget B, Bernardini S, Homsak E. Lessons Learned from the COVID-19 Pandemic: Emphasizing the Emerging Role and Perspectives from Artificial Intelligence, Mobile Health, and Digital Laboratory Medicine. EJIFCC 2021; 32:224-243. [PMID: 34421492 PMCID: PMC8343043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
SARS-CoV-2, the new coronavirus causing COVID-19, is one of the most contagious disease of past decades. COVID-19 is different only in that everyone is encountering it for the first time during this pandemic. The world has gone from complete ignorance to a blitz of details in a matter of months. The foremost challenge that the scientific community faces is to understand the growth and transmission capability of the virus. As the world grapples with the global pandemic, people are spending more time than ever before living and working in the digital milieu, and the adoption of Artificial Intelligence (AI) is propelled to an unprecedented level especially as AI has already proven to play an important role in counteracting COVID-19. AI and Data Science are rapidly becoming important tools in clinical research, precision medicine, biomedical discovery and medical diagnostics. Machine learning (ML) and their subsets, such as deep learning, are also referred to as cognitive computing due to their foundational basis and relationship to cognition. To date, AI based techniques are helping epidemiologists in projecting the spread of virus, contact tracing, early detection, monitoring, social distancing, compiling data and training of healthcare workers. Beside AI, the use of telemedicine, mobile health or mHealth and the Internet of Things (IOT) is also emerging. These techniques have proven to be powerful tools in fighting against the pandemic because they provide strong support in pandemic prevention and control. The present study highlights applications and evaluations of these technologies, practices, and health delivery services as well as regulatory and ethical challenges regarding AI/ML-based medical products.
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Affiliation(s)
- Pradeep Kumar Dabla
- Department of Biochemistry, G.B. Pant Institute of Postgraduate Medical Education & Research, Associated Maulana Azad Medical College, New Delhi, India
- Emerging Technologies Division - MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC)
| | - Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires Saint Luc, Catholic University of Louvain, Brussels, Belgium
- Research unit on Endocrinology Diabetes and Nutrition, Catholic University of Louvain, Brussels, Belgium
- Emerging Technologies Division - MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC)
| | - Bernard Gouget
- Healthcare Division Committee, Comité Français d’accréditation (Cofrac), & National Committee for the selection of Reference Laboratories, Ministry of Health, Paris, France
- Emerging Technologies Division - MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC)
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
- Emerging Technologies Division - MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC)
| | - Evgenija Homsak
- Emerging Technologies Division - MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC)
- Department for Laboratory Diagnostics, University Clinical Center Maribor, Maribor, Slovenia
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Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021; 91:101933. [PMID: 34082281 PMCID: PMC8123377 DOI: 10.1016/j.compmedimag.2021.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 04/27/2021] [Indexed: 12/13/2022]
Abstract
The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.
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Affiliation(s)
- Mustafa Alhasan
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates; Radiologic Technology Program, Applied Medical Sciences College, Jordan University of Science and Technology, Jordan.
| | - Mohamed Hasaneen
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates.
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Thomas MJ, Lal V, Baby AK, Rabeeh Vp M, James A, Raj AK. Can technological advancements help to alleviate COVID-19 pandemic? a review. J Biomed Inform 2021; 117:103787. [PMID: 33862231 PMCID: PMC8056973 DOI: 10.1016/j.jbi.2021.103787] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/22/2021] [Accepted: 04/10/2021] [Indexed: 12/18/2022]
Abstract
The COVID-19 pandemic is continuing, and the innovative and efficient contributions of the emerging modern technologies to the pandemic responses are too early and cannot be completely quantified at this moment. Digital technologies are not a final solution but are the tools that facilitate a quick and effective pandemic response. In accordance, mobile applications, robots and drones, social media platforms (such as search engines, Twitter, and Facebook), television, and associated technologies deployed in tackling the COVID-19 (SARS-CoV-2) outbreak are discussed adequately, emphasizing the current-state-of-art. A collective discussion on reported literature, press releases, and organizational claims are reviewed. This review addresses and highlights how these effective modern technological solutions can aid in healthcare (involving contact tracing, real-time isolation monitoring/screening, disinfection, quarantine enforcement, syndromic surveillance, and mental health), communication (involving remote assistance, information sharing, and communication support), logistics, tourism, and hospitality. The study discusses the benefits of these digital technologies in curtailing the pandemic and 'how' the different sectors adapted to these in a shorter period. Social media and television's role in ensuring global connectivity and serving as a common platform to share authentic information among the general public were summarized. The World Health Organization and Governments' role globally in-line with the prevention of propagation of false news, spreading awareness, and diminishing the severity of the COVID-19 was discussed. Furthermore, this collective review is helpful to investigators, health departments, Government organizations, and policymakers alike to facilitate a quick and effective pandemic response.
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Affiliation(s)
- Mervin Joe Thomas
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Vishnu Lal
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Ajith Kurian Baby
- Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Muhammad Rabeeh Vp
- School of Materials Science and Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Alosh James
- Solar Energy Center, Dept. of Mechanical Engg., National Institute of Technology Calicut, Kerala 673601, India
| | - Arun K Raj
- Dept. of Mechanical Engg., Indian Institute of Technology Bombay, Maharashtra 400076, India.
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Niang M, Dupéré S, Alami H, Gagnon MP. Why is repositioning public health innovation towards a social paradigm necessary? A reflection on the field of public health through the examples of Ebola and Covid-19. Global Health 2021; 17:46. [PMID: 33853631 PMCID: PMC8045578 DOI: 10.1186/s12992-021-00695-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/29/2021] [Indexed: 01/01/2023] Open
Abstract
Health innovations are generally oriented on a techno-economic vision. In this perspective, technologies are seen as an end in themselves, and there is no arrangement between the technical and the social values of innovation. This vision prevails in sanitary crises, in which management is carried out based on the search for punctual, reactive, and technical solutions to remedy a specific problem without a systemic/holistic, sustainable, or proactive approach. This paper attempts to contribute to the literature on the epistemological orientation of innovations in the field of public health. Taking the Covid-19 and Ebola crises as examples, the primary objective is to show how innovation in health is oriented towards a techno-economic paradigm. Second, we propose a repositioning of public health innovation towards a social paradigm that will put more emphasis on the interaction between social and health dimensions in the perspective of social change. We will conclude by highlighting the roles that public health could play in allowing innovations to have more social value, especially during sanitary crises.
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Affiliation(s)
- Marietou Niang
- Faculty of Nursing Science, Université Laval, 1050, Avenue de la Médecine, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6 Canada
| | - Sophie Dupéré
- Faculty of Nursing Science, Université Laval, 1050, Avenue de la Médecine, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6 Canada
| | - Hassane Alami
- Center for Public Health Research, Université de Montréal, Montreal, Québec Canada
| | - Marie-Pierre Gagnon
- Faculty of Nursing Science, Université Laval, 1050, Avenue de la Médecine, Pavillon Ferdinand-Vandry, Québec, QC G1V 0A6 Canada
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43
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Javaid M, Khan IH. Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. J Oral Biol Craniofac Res 2021; 11:209-214. [PMID: 33665069 PMCID: PMC7897999 DOI: 10.1016/j.jobcr.2021.01.015] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 01/23/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND/OBJECTIVES The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions. METHODS Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. RESULTS Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them. CONCLUSIONS In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.
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Affiliation(s)
- Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
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Park S, Kim B, Kim KA. Preventive Behavioral Insights for Emerging Adults: A Survey during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052569. [PMID: 33806586 PMCID: PMC7967644 DOI: 10.3390/ijerph18052569] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/16/2021] [Accepted: 02/22/2021] [Indexed: 12/24/2022]
Abstract
Emerging adulthood is an important period for establishing health behavior patterns in life. This study aimed to examine factors related to preventive behaviors of emerging adults during the COVID-19 pandemic. A descriptive online survey design was used. Data were collected using a self-administrated, 28-item questionnaire completed by 239 undergraduate students from a university in Seoul, South Korea. The questionnaire was developed based on previous studies and the guidelines of the World Health Organization about COVID-19 preventive behaviors. The mean age of participants was 21.97 years, and the average score for COVID-19 preventive behaviors was 4.13 (SD: ±0.42) on a 5-point scale. Hierarchical regression analyses revealed that subjective norms related to parents (β = 0.425, p < 0.001), issue involvement related to COVID-19 (β = 0.160, p = 0.024), and sex (β = 0.137, p = 0.029) were significant factors related to preventive behaviors of emerging adults after controlling for demographic characteristics. The variables explained 20.1% of the variance in preventive behaviors. The results of this study suggest that better strategies for subjective norms related to parents and issue involvement related to COVID-19 must be considered to improve emerging adults’ preventive behaviors.
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Affiliation(s)
- Sunhee Park
- Barun ICT Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-Gu, Seoul 03722, Korea;
| | - Beomsoo Kim
- Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-Gu, Seoul 03722, Korea;
| | - Kyoung A. Kim
- Department of Nursing, Yeoju Institute of Technology 338, Sejong-ro, Yeoju-si 12652, Korea
- Correspondence: ; Tel.: +82-31-880-5519
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Elzeki OM, Abd Elfattah M, Salem H, Hassanien AE, Shams M. A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset. PeerJ Comput Sci 2021; 7:e364. [PMID: 33817014 PMCID: PMC7959632 DOI: 10.7717/peerj-cs.364] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/30/2020] [Indexed: 05/31/2023]
Abstract
BACKGROUND AND PURPOSE COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. MATERIALS AND METHODS In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. RESULTS Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. CONCLUSIONS A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.
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Affiliation(s)
- Omar M. Elzeki
- Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | | | - Hanaa Salem
- Communications and Computers Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Mahmoud Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
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Chaudhary V, Royal A, Chavali M, Yadav SK. Advancements in research and development to combat COVID-19 using nanotechnology. NANOTECHNOLOGY FOR ENVIRONMENTAL ENGINEERING 2021. [PMCID: PMC7829094 DOI: 10.1007/s41204-021-00102-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The whole world is currently facing a global health crisis due to the coronavirus disease (COVID-19) pandemic caused by SARS coronavirus 2, which started in Wuhan City, China, in December 2019. The pandemic has affected 235 countries, areas or territories and infected over 42 million people across the globe as per WHO update on 27 October 2020. More than 1.1 million people have died and the numbers are increasing daily. However, some drugs have been authorized for emergency treatment of patients, medication and vaccines with proven efficacy to prevent and treat the disease is still under various phases of development. The entire world is consistently making efforts to address three major challenges related to COVID-19 including prevention of its spread, prompt and early diagnosis and treatment of patients to save lives. Touted as one of the game-changing technologies of the century, nanotechnology has huge potential to develop solutions against these three major challenges of the disease. Nanotechnology comprises of multidisciplinary prospects encompassing diverse disciplines including medicine, material science, artificial intelligence, environment, virology, physical sciences, chemistry and biology. The numerous challenges can be addressed through the engineering of the various physicochemical properties of materials presents in abundance in nature. Various claims, studies and reports on research and development to combat these challenges associated with COVID-19 have been collectively discussed in this article from the perspectives of nanotechnology.
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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Fedorowski JJ. Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform? Arch Med Sci 2021; 17:827-828. [PMID: 34025855 PMCID: PMC8130463 DOI: 10.5114/aoms/134716] [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: 03/14/2021] [Accepted: 03/21/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza. METHODS The article is the author's original research hypothesis. RESULTS Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells. CONCLUSIONS A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.
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Affiliation(s)
- Jaroslaw J. Fedorowski
- Polish Hospital Federation, Poland
- Collegium Humanum Warsaw Management University, Warsaw, Poland
- College of Medicine and Health Network, University of Vermont, Vermont, United States
- Warsaw Maria Curie-Sklodowska Medical University, Warsaw, Poland
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Unbundling the significance of cognitive robots and drones deployed to tackle COVID-19 pandemic: A rapid review to unpack emerging opportunities to improve healthcare in sub-Saharan Africa. COGNITIVE ROBOTICS 2021. [PMCID: PMC8595978 DOI: 10.1016/j.cogr.2021.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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50
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Shen Y, Guo D, Long F, Mateos LA, Ding H, Xiu Z, Hellman RB, King A, Chen S, Zhang C, Tan H. Robots Under COVID-19 Pandemic: A Comprehensive Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 9:1590-1615. [PMID: 34976569 PMCID: PMC8675561 DOI: 10.1109/access.2020.3045792] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 12/08/2020] [Indexed: 05/04/2023]
Abstract
As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.
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Affiliation(s)
- Yang Shen
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Dejun Guo
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Fei Long
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Luis A. Mateos
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Houzhu Ding
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Zhen Xiu
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | | | - Adam King
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Shixun Chen
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Chengkun Zhang
- UBTECH North America Research and Development CenterPasadenaCA91101USA
| | - Huan Tan
- UBTECH North America Research and Development CenterPasadenaCA91101USA
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