ArticleIEEE internet of things journal2021
Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access.
Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 79 citations in OpenAlex.
- Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson's disease.PloS one · 2026Article
- Telerobotic surgery: a comprehensive two-decade evolution and the integration of emerging technologies.International journal of surgery (London, England) · 2026Review
- From Innovation to Integration: Bridging the Gap Between IoMT Technologies and Real-World Health Management Systems.Sensors (Basel, Switzerland) · 2025Article
- Review
- Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?Diagnostics (Basel, Switzerland) · 2024Review
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods.International journal of environmental research and public health · 2023Article
- A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.Scientific reports · 2023Article
- A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives.Medicina (Kaunas, Lithuania) · 2022Review
- A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models.Scientific reports · 2022Article
- Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset.Scientific reports · 2022Article
- Secure Telemedicine System Design for COVID-19 Patients Treatment Using Service Oriented Architecture.Sensors (Basel, Switzerland) · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
- FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone's guiding images with the Green Zone's view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.