Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access

A comprehensive study on unraveling the advances of immersive technologies (VR/AR/MR/XR) in the healthcare sector during the COVID-19: Challenges and solutions

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

Active Reconfigurable Intelligent Surface Enhanced Internet of Medical Things

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome "double-fading" effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.

Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR's integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR's transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm

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.

Hybrid optimized feature selection and deep learning based COVID-19 disease prediction

The COVID-19 virus has affected many people around the globe with several issues. Moreover, it causes a worldwide pandemic, and it makes more than one million deaths. Countries around the globe had to announce a complete lockdown when the corona virus causes the community to spread. In real-time, Polymerase Chain Reaction (RT-PCR) test is conducted to detect COVID-19, which is not effective and sensitive. Hence, this research presents the proposed Caviar-MFFO-assisted Deep LSTM scheme for COVID-19 detection. In this research, the COVID-19 cases data is utilized to process the COVID-19 detection. This method extracts the various technical indicators that improve the efficiency of COVID-19 detection. Moreover, the significant features fit for COVID-19 detection are selected using proposed mayfly with fruit fly optimization (MFFO). In addition, COVID-19 is detected by Deep Long Short Term Memory (Deep LSTM), and the Conditional Autoregressive Value at Risk MFFO (Caviar-MFFO) is modeled to train the weight of Deep LSTM. The experimental analysis reveals that the proposed Caviar-MFFO assisted Deep LSTM method provided efficient performance based on the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and achieved the recovered cases with the minimal values of 1.438 and 1.199, whereas the developed model achieved the death cases with the values of 4.582 and 2.140 for MSE and RMSE. In addition, 6.127 and 2.475 are achieved by the developed model based on infected cases.

Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

Machine learning applications for COVID-19 outbreak management

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.

Technology Behavior Model—Beyond Your Sight with Extended Reality in Surgery

Extended Reality Smart Glasses is a new pattern that uses extended reality technology to present a visual environment that combines the physical and virtual worlds. However, the surgical technique using Smart Glasses implementation is still unknown, to the infancy in clinical surgery, derived to the limits of existing technology. This study researched the acceptability and possibility of XRSG for medical experts. It combines human seen behavioral control with information technology research to construct a new “Extended Reality Technology Behavior Model” using method Technology Acceptance Model and Theory of Planned Behavior. To improve the accuracy of the study, statistical analysis, exploratory analysis, and cross-sectional research triangulation were used to collect data in five hospitals in Malaysia using a convenience sampling method and a questionnaire on behavioral influences. From the collected data, PLS-SEM analysis was used to reflect the relationship between variables. The strong positive results suggest that using XRSG by medical experts helps to improve the composition, interactivity, standardization, and clarity of medical images, resulting in increased efficiency and reduced procedure time and felt the usefulness and ease of use of XRSG through their behavior, providing a basis for technology acceptance in surgery.

A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives

A coronavirus outbreak caused by a novel virus known as SARS-CoV-2 originated towards the latter half of 2019. COVID-19's abrupt emergence and unchecked global expansion highlight the inability of the current healthcare services to respond to public health emergencies promptly. This paper reviews the different aspects of human life comprehensively affected by COVID-19. It then discusses various tools and technologies from the leading domains and their integration into people's lives to overcome issues resulting from pandemics. This paper further focuses on providing a detailed review of existing and probable Artificial Intelligence (AI), Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and Blockchain-based solutions. The COVID-19 pandemic brings several challenges from the viewpoint of the nation's healthcare, security, privacy, and economy. AI offers different predictive services and intelligent strategies for detecting coronavirus signs, promoting drug development, remote healthcare, classifying fake news detection, and security attacks. The incorporation of AI in the COVID-19 outbreak brings robust and reliable solutions to enhance the healthcare systems, increases user's life expectancy, and boosts the nation's economy. Furthermore, AR/VR helps in distance learning, factory automation, and setting up an environment of work from home. Blockchain helps in protecting consumer's privacy, and securing the medical supply chain operations. IoT is helpful in remote patient monitoring, distant sanitising via drones, managing social distancing (using IoT cameras), and many more in combating the pandemic. This study covers an up-to-date analysis on the use of blockchain technology, AI, AR/VR, and IoT for combating COVID-19 pandemic considering various applications. These technologies provide new emerging initiatives and use cases to deal with the COVID-19 pandemic. Finally, we discuss challenges and potential research paths that will promote further research into future pandemic outbreaks.

A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.

Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined the term, "multi-site split learning", which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.

Secure Telemedicine System Design for COVID-19 Patients Treatment Using Service Oriented Architecture

The coronavirus pandemic, also known as the COVID-19 pandemic, is an ongoing virus. It was first identified on December 2019 in Wuhan, China, and later spread to 192 countries. As of now, 251,266,207 people have been affected, and 5,070,244 deaths are reported. Due to the growing number of COVID-19 patients, the demand for COVID wards is increasing. Telemedicine applications are increasing drastically because of convenient treatment options. The healthcare sector is rapidly adopting telemedicine applications for the treatment of COVID-19 patients. Most telemedicine applications are developed for heterogeneous environments and due to their diverse nature, data transmission between similar and dissimilar telemedicine applications is a difficult task. In this paper, we propose a Tele-COVID system architecture design along with its security aspects to provide the treatment for COVID-19 patients from distance. Tele-COVID secure system architecture is designed to resolve the problem of data interchange between two different telemedicine applications, interoperability, and vendor lock-in. Tele-COVID is a web-based and Android telemedicine application that provides suitable treatment to COVID-19 patients. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. The application is tested on COVID-19 patients in the county hospital and shows the initial results.

Deep Learning Hybrid Models for COVID-19 Prediction

COVID-19 is a highly contagious virus. Blood test is one of effective method for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staffs. In this paper, four deep learning hybrid models are proposed to address these issues, i.e., CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM, CNN+Bi-GRU. Besides, two best models CNN and CNN+LSTM from Turabieh et al. and Alakus et al. are implemented, respectively. Blood test data from Hospital Israelita Albert Einstein is used to train and test six models. The proposed best model CNN+Bi-GRU is accuracy of 0.9415, precision of 0.9417, recall of 0.9417, F1-score of 0.9417, AUC of 0.91, which outperforms the best models from Turabieh et al. and Alakus et al. Furthermore, the proposed model can help patients to get blood test results faster than traditional manual tests, and do not have errors caused by fatigue. We can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.

Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring

The widespread adoption of wearables, extended reality, and metaverses has accelerated the diverse configurations of remote collaboration and telementoring systems. This paper explores the opportunities and challenges of interactive telementoring, especially for wearers of smartglasses. In particular, recent relevant studies are reviewed to derive the needs and trends of telementoring technology. Based on this analysis, we define what can be integrated into smartglass-enabled interactive telementoring. To further illustrate this type of special use case for telementoring, we present five illustrative and descriptive scenarios. We expect our specialized use case to support various telementoring applications beyond medical and surgical telementoring, while harmoniously fostering cooperation using the smart devices of mentors and mentees at different scales for collocated, distributed, and remote collaboration.

A deep neural network based context-aware smart epidemic surveillance in smart cities

Purpose

Epidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.

Design/methodology/approach

A deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.

Findings

The experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.

Originality/value

The proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason, that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause privacy leakage. To solve this problem, we adopt the Federated Learning (FL) framework, a new technique being used to protect data privacy. Under the FL framework and Differentially Private thinking, we propose a Federated Differentially Private Generative Adversarial Network (FedDPGAN) to detect COVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of the training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, the evaluation of the proposed model is on three types of chest X-ray (CXR)images dataset (COVID-19, normal, and normal pneumonia). A large number of truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

Research on COVID-19 Prevention and Control Model Based on Evolutionary Games

In view of epidemic prevention costs and social benefits, an evolutionary game model of epidemic prevention and control strategies between government departments and local people was constructed based on evolutionary game theory to explore the influence of strategic behaviors between government departments and local people, and MATLAB was used to conduct systematic simulation of the game model. Studies have shown that local people will cooperate with government departments when they implement surveillance strategies. Reducing the cost of emergency epidemic prevention, and improving the social benefits of epidemic prevention are conducive to the development of government departments towards the direction of supervision strategy, and local people towards the direction of active epidemic prevention strategy, so as to achieve effective epidemic prevention and control.