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Cheng KY, Lange-Hegermann M, Hövener JB, Schreiweis B. Instance-level medical image classification for text-based retrieval in a medical data integration center. Comput Struct Biotechnol J 2024; 24:434-450. [PMID: 38975287 PMCID: PMC11226965 DOI: 10.1016/j.csbj.2024.06.006] [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: 02/29/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
A medical data integration center integrates a large volume of medical images from clinical departments, including X-rays, CT scans, and MRI scans. Ideally, all images should be indexed appropriately with standard clinical terms. However, some images have incorrect or missing annotations, which creates challenges in searching and integrating data centrally. To address this issue, accurate and meaningful descriptors are needed for indexing fields, enabling users to efficiently search for desired images and integrate them with international standards. This paper aims to provide concise annotation for missing or incorrectly indexed fields, incorporating essential instance-level information such as radiology modalities (e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g., lateral) using a Deep Learning classification model - ResNet50. To demonstrate the capabilities of our algorithm in generating annotations for indexing fields, we conducted three experiments using two open-source datasets, the ROCO dataset, and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels. While the outcomes of these experiments are satisfactory (Precision of >75%) for less critical tasks and serve as a valuable testing ground for image retrieval, they also underscore the need for further exploration of potential challenges. This essay elaborates on the identified issues and presents well-founded recommendations for refining and advancing our proposed approach.
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Affiliation(s)
- Ka Yung Cheng
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Jan-Bernd Hövener
- Department of Radiology and Neuroradiology, Section Biomedical Imaging, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Björn Schreiweis
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
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Wang Z, Zhao D, Heidari AA, Chen Y, Chen H, Liang G. Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation. Sci Rep 2024; 14:13239. [PMID: 38853172 PMCID: PMC11163015 DOI: 10.1038/s41598-024-63739-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024] Open
Abstract
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.
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Affiliation(s)
- Zhen Wang
- College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, Jilin, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, Jilin, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.
| | - Guoxi Liang
- Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
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Kumar V, Prabha C, Sharma P, Mittal N, Askar SS, Abouhawwash M. Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images. BMC Med Imaging 2024; 24:63. [PMID: 38500083 PMCID: PMC10946139 DOI: 10.1186/s12880-024-01241-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/07/2024] [Indexed: 03/20/2024] Open
Abstract
Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.
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Affiliation(s)
- Vinod Kumar
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India
| | - Chander Prabha
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Preeti Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, Haryana, India.
| | - S S Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Ha J, Yoon DY, Baek S, Lee CW, Lim KJ, Seo YL, Yun EJ. The 100 most-cited and 100 most-mentioned COVID-19-related radiological articles: a comparative bibliometric analysis. Eur Radiol 2024; 34:1167-1175. [PMID: 37581662 DOI: 10.1007/s00330-023-10001-x] [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: 12/06/2022] [Revised: 05/23/2023] [Accepted: 06/05/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVES This study aimed to identify the 100 most-cited and 100 most-mentioned coronavirus disease-2019 (COVID-19)-related radiological articles and compare their characteristics. MATERIALS AND METHODS We searched the Web of Science and Altmetric.com using the search terms "COVID," "COVID-19," "Coronavirus," "SARS-CoV-2," "nCoV," and "pandemic" to identify the most-cited and most-mentioned COVID-19-related articles. We identified the top 100 most-cited and 100 most-mentioned articles in the field of radiology, regardless of their publication journal. We extracted the information from the listed articles and compared the characteristics between the most-cited and most-mentioned. RESULTS Thirty (30%) articles were featured in the lists of the most-cited and most-mentioned articles. The comparison of the 100 most-cited and most-mentioned articles on each list showed that the most frequently cited articles were published in November 2020 and before (p < .001), originated from China (p < .001), covered the topic of diagnosis of COVID-19 (p < .001), and were related to the subspecialty of pulmonary imaging (p < .001); the most frequently mentioned articles were published in December 2020 and after (p < .001), originated from the USA (p < .001), covered the topic of diagnosis of sequelae of COVID-19 (p = .013) and post-vaccination complications (p < .001), and were related to the subspecialties of cardiac imaging (p < .001) and neuroradiology (p < .013). CONCLUSION Significant differences were observed in publication date, country of origin, topic, and subspecialty of scientific knowledge related to COVID-19 in the field of radiology, between citation and public dissemination. CLINICAL RELEVANCE STATEMENT This bibliometric analysis compares the 100 most-cited and 100 most-mentioned COVID-19-related radiologic articles, aiming to provide valuable insights into the patterns of knowledge dissemination during the pandemic era. KEY POINTS • Thirty articles were featured on the lists of the 100 most-cited and 100 most-mentioned COVID-19-related articles. • The 70 unique most-cited articles more frequently originated from China (48.6%), while the unique most-mentioned articles more frequently originated from the USA (51.4%) (p < 0.001). • The 70 unique most-mentioned articles were more frequently related to cardiac imaging (25.7% vs.0%, p < 0.001) and neuroradiology (15.7% vs. 1.4%, p < 0.005) compared to the unique most-mentioned articles.
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Affiliation(s)
- Jiyeon Ha
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea
| | - Dae Young Yoon
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea.
| | - Sora Baek
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea.
| | - Chae Woon Lee
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea
| | - Kyoung Ja Lim
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea
| | - Young Lan Seo
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea
| | - Eun Joo Yun
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, 150, Seongan-Ro, Gangdong-Gu, Seoul, 05355, Republic of Korea
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Zhang M, Wang Y, Lv M, Sang L, Wang X, Yu Z, Yang Z, Wang Z, Sang L. Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis. Technol Cancer Res Treat 2024; 23:15330338241235769. [PMID: 38465611 DOI: 10.1177/15330338241235769] [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] [Indexed: 03/12/2024] Open
Abstract
Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.
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Affiliation(s)
- Minghui Zhang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Yan Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Mutian Lv
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Li Sang
- Department of Acupuncture and Massage, Shouguang Hospital of Traditional Chinese Medicine, Weifang, P. R. China
| | - Xuemei Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zijun Yu
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Ziyi Yang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zhongqing Wang
- Department of Information Center, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Liang Sang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
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Ahmadinejad N, Ayyoubzadeh SM, Zeinalkhani F, Delazar S, Javanmard Z, Ahmadinejad Z, Mohajeri A, Esmaeili M. Discovering associations between radiological features and COVID-19 patients' deterioration. Health Sci Rep 2023; 6:e1257. [PMID: 37711676 PMCID: PMC10497911 DOI: 10.1002/hsr2.1257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/17/2023] [Accepted: 04/23/2023] [Indexed: 09/16/2023] Open
Abstract
Background and Aims Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Methods This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.
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Affiliation(s)
- Nasrin Ahmadinejad
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Fahimeh Zeinalkhani
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Sina Delazar
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
| | - Zohreh Javanmard
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Zahra Ahmadinejad
- Department of Infectious Diseases, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran
| | | | - Marzieh Esmaeili
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
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Gu J, Qian X, Zhang Q, Zhang H, Wu F. Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance. Comput Biol Med 2023; 164:107207. [PMID: 37480680 DOI: 10.1016/j.compbiomed.2023.107207] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/06/2023] [Accepted: 06/25/2023] [Indexed: 07/24/2023]
Abstract
Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been extensively developed. However, deep learning relies on many samples with high-quality labels, which is expensive. To this end, we propose a novel unsupervised domain adaptation method to process many different but related Covid-19 X-ray images. Unlike existing unsupervised domain adaptation methods that cannot handle conditional class distributions, we adopt a balanced Slice Wasserstein distance as the metric for unsupervised domain adaptation to solve this problem. Multiple standard datasets for domain adaptation and X-ray datasets of different Covid-19 are adopted to verify the effectiveness of our proposed method. Experimented by cross-adopting multiple datasets as source and target domains, respectively, our proposed method can effectively capture discriminative and domain-invariant representations with better data distribution matching.
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Affiliation(s)
- Jiawei Gu
- Affiliated Hospital of Nantong University, Nantong, 226001, China.
| | - Xuan Qian
- Affiliated Hospital of Nantong University, Nantong, 226001, China.
| | - Qian Zhang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325035, China.
| | - Hongliang Zhang
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Fang Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, China.
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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Shukla AK, Seth T, Muhuri PK. Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-33. [PMID: 37362722 PMCID: PMC9978294 DOI: 10.1007/s11042-023-14642-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/01/2022] [Accepted: 02/03/2023] [Indexed: 06/28/2023]
Abstract
With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.
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Affiliation(s)
- Amit K. Shukla
- Faculty of Information Technology, University of Jyväskylä, Box 35 (Agora), Jyväskylä, 40014 Finland
| | - Taniya Seth
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
| | - Pranab K. Muhuri
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
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Nilashi M, Ali Abumalloh R, Mohd S, Nurlaili Farhana Syed Azhar S, Samad S, Hang Thi H, Alghamdi OA, Alghamdi A. COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context. TELEMATICS AND INFORMATICS 2023; 76:101923. [PMID: 36510580 PMCID: PMC9729173 DOI: 10.1016/j.tele.2022.101923] [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: 08/09/2022] [Revised: 10/22/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022]
Abstract
The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
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Affiliation(s)
- Mehrbakhsh Nilashi
- UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia
- Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Rabab Ali Abumalloh
- Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia
| | - Saidatulakmal Mohd
- School of Social Sciences, Universiti Sains Malaysia, USM Penang, Malaysia
| | | | - Sarminah Samad
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ha Hang Thi
- Institute of Research and Development, Duy Tan University, Da Nang, VietNam
- International School, Duy Tan University, Da Nang, VietNam
| | - O A Alghamdi
- Business Administration Dept., Applied College, Najran University, Najran, Saudi Arabia
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
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Band SS, Ardabili S, Yarahmadi A, Pahlevanzadeh B, Kiani AK, Beheshti A, Alinejad-Rokny H, Dehzangi I, Chang A, Mosavi A, Moslehpour M. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis. Front Public Health 2022; 10:869238. [PMID: 35812486 PMCID: PMC9260273 DOI: 10.3389/fpubh.2022.869238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.
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Affiliation(s)
- Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Sina Ardabili
- Department of Informatics, J. Selye University, Komárom, Slovakia
| | - Atefeh Yarahmadi
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Bahareh Pahlevanzadeh
- Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran
| | - Adiqa Kausar Kiani
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia
- U.N.S.W. Data Science Hub, The University of New South Wales (U.N.S.W. Sydney), Sydney, NSW, Australia
- Health Data Analytics Program, AI-enabled Processes (A.I.P.) Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, United States
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Massoud Moslehpour
- Department of Business Administration, College of Management, Asia University, Taichung, Taiwan
- Department of Management, California State University, San Bernardino, CA, United States
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12
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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13
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Zhang Y, Hu M, Wang J, Wang P, Shi P, Zhao W, Liu X, Peng Q, Meng B, Feng X, Zhang L. A Bibliometric Analysis of Personal Protective Equipment and COVID-19 Researches. Front Public Health 2022; 10:855633. [PMID: 35570977 PMCID: PMC9099374 DOI: 10.3389/fpubh.2022.855633] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/29/2022] [Indexed: 01/09/2023] Open
Abstract
COVID-19, which occurred at the end of December 2019, has evolved into a global public health threat and affects every aspect of human life. COVID-19's high infectivity and mortality prompted governments and the scientific community to respond quickly to the pandemic outbreak. The application of personal protective equipment (PPE) is of great significance in overcoming the epidemic situation. Since the discovery of severe acute respiratory coronavirus 2 (SARS-CoV-2), bibliometric analysis has been widely used in many aspects of the COVID-19 epidemic. Although there are many reported studies about PPE and COVID-19, there is no study on the bibliometric analysis of these studies. The citation can be used as an indicator of the scientific influence of an article in its field. The aim of this study was to track the research trends and latest hotspots of COVID-19 in PPE by means of bibliometrics and visualization maps.
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Affiliation(s)
- Yu Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Man Hu
- Department of Orthopedics, Dalian Medical University, Dalian, China
| | - Junwu Wang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Pingchuan Wang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Pengzhi Shi
- Department of Orthopedics, Dalian Medical University, Dalian, China
| | - Wenjie Zhao
- Department of Orthopedics, Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Qing Peng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Bo Meng
- Department of Orthopedics, Dalian Medical University, Dalian, China
| | - Xinmin Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
- *Correspondence: Liang Zhang
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