Internet-of-Things-Enabled Infrastructure Against Infectious Diseases

Memos, Vasileios A.; Minopoulos, Georgios; Stergiou, Konstantinos D.; Psannis, Kostas E.

doi:10.1109/iotm.0001.2100023

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…However, the combination of visualization technologies and AI still faces many limitations and obstacles in the face of practical medical problems. For example, the validity of physiological data needs to be improved, the extensibility of methods needs to be evaluated, the current hardware capacity is limited, there is a lack of tactile feedback, data privacy issues are controversial, the application cost of some systems may beyond affordability, and there is still a gap between the progress made by neophytes in simulated operation training and clinical practice; the latter is somewhat risky but the trainers make greater progress [44][45][46][47][48]. This requires the collaboration of doctors, AI engineers, and other professionals to develop a more powerful multi-modal treatment system.…”

Section: Burst Abstractmentioning

confidence: 99%

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years

Zuo,

Wang,

Wan

et al. 2024

Healthcare

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Background: Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR’s potential uses and research directions in medicine. Methods: Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. Results: A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as “task analysis”, “deep learning”, and “machine learning”. Conclusion: The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers.

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Section: Burst Abstractmentioning

confidence: 99%

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years

Zuo,

Wang,

Wan

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…The classification is a type of supervised learning method [70][71][72][73]. Recently, there are many applications of machine learning classification algorithms detailed in [69][70][71][72][73][74][75]. It is a categorization method that distinguishes, predicts, and understands objects and ideas.…”

Section: 7classificationmentioning

confidence: 99%

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

Yang

Por

Khan

et al. 2023

Preprint

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Lung cancer detection using machine learning involves training a model on a dataset of medical images, such as CT scans, to identify patterns and features associated with lung cancer. Past researchers developed different computer aided diagnostic (CAD) systems for early prediction of lung cancer. The researchers extracted single features such as texture, morphology etc.; however, by combining the features, accuracy can be improved. In this study, we extracted Gray-level co-occurrence (GLCM), autoencoder and Haralick texture features. We combined these features and computed the performance using robust machine algorithms including Decision tree (DT), Naïve Bayes (NB) and support vector machine (SVM) with different kernel functions. The performance was evaluated using standard performance measures. The hybrid methods such as GLCM + Autoencoder, and Haralick + Autoencoder yielded highest detection performance using SVM Gaussian and radial base function (RBF) with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) with accuracy of 100% and AUC 1.00 followed by SVM polynomial yielded an accuracy of 99.89% and AUC of 1.00; GLCM + Haralick using SVM Gaussian yielded accuracy (99.56%), SVM RBF yielded accuracy (99.35%). The results reveal that the proposed feature extraction methodology can be usefully used to predict the lung cancer for further diagnosis at early stage. MSC: Artificial Intelligence, Machine Learning, Lung Cancer, cross validation

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“…We identified several original, exploratory studies on infectious diseases targeting social media users between 1 January 2000 and 30 June 2017. Both Twitter and other blogs claimed to be seen as valuable social media data sources in infectious disease surveillance ( 31 – 34 ).…”

Section: The Application Of Internet Search Datamentioning

confidence: 99%

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

Sun¹,

Zhang²,

Gao³

et al. 2022

Front. Public Health

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With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

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Internet-of-Things-Enabled Infrastructure Against Infectious Diseases

Cited by 10 publications

References 14 publications

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years

Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

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