Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

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.

Intelligent deep learning-based disease monitoring system in 5G network using multi-disease big data

Recently, real-world disease monitoring techniques designed based on wearable medical equipment efficiently minimize the mortality rate. Initially, the data are manually collected from the patients to predict five diseases using 5 G frameworks. Then, the collected data are pre-processed to obtain high-quality data using the techniques like contrast enhancement, median filtering, fill empty space, remove repeated value and stemming. The pre-processed data are taken for extracting the features using a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the deep features. The parameters like hidden neuron count and epoch are tuned by the proposed Modified Predator Presence Probability-based Squirrel Search-Glowworm Swarm Optimization (MPPP-SSGSO) algorithm to enhance the variance. Then, the extracted features acquired using the 1D-CNN are given to the ensemble boosting-based models for predicting the score, which is combined by comprising approaches like Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost) and Category Boosting (CatBoost). Further, the predicted scores obtained from such models are concatenated and passed to the Ensemble Boosting Scores-based Fuzzy Classifier (EBS-FC) for classifying the five different diseases. Here, the membership function of the fuzzy is optimized by the same developed MPPP-SSGSO algorithm for enhancing accuracy. Experiments are conducted, and validation is performed, which showcased that the recommended framework achieved a better outcome rate than the conventional techniques. Finally, the suggested strategy outperforms the current state-of-the-art methods with an accuracy rate of 91.34%.Communicated by Ramaswamy H. Sarma.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care

BACKGROUND

e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients.

OBJECTIVE

The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic.

METHODS

The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients.

RESULTS

Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model).

CONCLUSIONS

Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Advanced Mobile Communication Techniques in the Fight against the COVID-19 Pandemic Era and Beyond: An Overview of 5G/B5G/6G

The coronavirus disease 2019 (COVID-19) pandemic has severely affected people's lives worldwide in an unexpected manner. According to the World Health Organization (WHO), several viral epidemics continue to occur and pose a significant public health problem. Until May 2023, there have been 676 million cases of COVID-19 infections and over 6.8 million deaths, globally. This paper surveys the role and effectiveness of advanced fifth-generation (5G)/beyond 5G (B5G)/sixth-generation (6G) technologies, combined with mobile applications (apps) and the Internet of Medical Things (IoMT), in detecting, managing, and mitigating the spread of COVID-19 and designing smart healthcare infrastructures for future pandemics. Analyzing and summarizing the research of relevant scholars based on the impact of 5G/B5G/6G and other technologies on COVID-19. The study tabulates the technical characteristics and effectiveness of different technologies in the context of COVID-19, summarizing the research of previous scholars. Challenges and design issues in the implementation of advanced information and telecommunication systems were demonstrated. These technologies can inspire the design of smart healthcare infrastructures to combat future virus pandemics.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features

In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

EEG-based emotion recognition using hybrid CNN and LSTM classification

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).

Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment

The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

Can information infrastructure development improve the health care environment? Evidence from China

Existing studies ignore the importance of information infrastructure development in improving regional health care environment. This paper adopts a spatial difference-in-difference (DID) model to assess the impact of information infrastructure development on urban health care environment based on a quasi-natural experiment of the "Broadband China" city pilots (BCCP). A balanced panel of 259 cities from 2010 to 2019 is selected for empirical analysis in this paper. Our findings show that the implementation of BCCP resulted in a 4.1 and 2.9% improvement in local medical workforce and medical infrastructure. In addition, there is significant spatial spillover effects of the implementation of BCCP, with 7.2 and 12.5% improvement in medical workforce and medical infrastructure in the surrounding areas. Our findings also suggest that information infrastructure development enhances the health care environment by driving industrial upgrading and education levels. Further analysis shows that BCCP has the strongest improvement on medical workforce in the eastern region and non-ordinary prefecture-level cities. For medical infrastructure, BCCP has stronger improvement in central region, western region, and non-ordinary prefecture-level cities. Finally, the paper conducts a series of robustness tests to ensure the reliability of the analysis results, including parallel trend tests, placebo tests, and re-estimation with different methods. Policies to improve the health care environment through information infrastructure development are proposed.

Application of 5G network combined with AI robots in personalized nursing in China: A literature review

The medical and healthcare industry is currently developing into digitization. Attributed to the rapid development of advanced technologies such as the 5G network, cloud computing, artificial intelligence (AI), and big data, and their wide applications in the medical industry, the medical model is shifting into an intelligent one. By combining the 5G network with cloud healthcare platforms and AI, nursing robots can effectively improve the overall medical efficacy. Meanwhile, patients can enjoy personalized medical services, the supply and the sharing of medical and healthcare services are promoted, and the digital transformation of the healthcare industry is accelerated. In this paper, the application and practice of 5G network technology in the medical industry are introduced, including telecare, 5G first-aid remote medical service, and remote robot applications. Also, by combining application characteristics of AI and development requirements of smart healthcare, the overall planning, intelligence, and personalization of the 5G network in the medical industry, as well as opportunities and challenges of its application in the field of nursing are discussed. This paper provides references to the development and application of 5G network technology in the field of medical service.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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.

5G in Healthcare: from COVID-19 to Future Challenges

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.

Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification

The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities. The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities). The proposed approach overlapping activity recognition using cluster-based classification (OAR-CbC) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level. Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level. We examine four partitioning and classification techniques with the same hierarchy for a fair comparison. The OAR-CbC evaluates on smart home datasets Aruba and Milan using threefold and leave-one-day-out cross-validation. We used evaluation metrics: precision, recall, F score, accuracy, and confusion matrices to ensure the model's reliability. The OAR-CbC shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Who are the ‘silent spreaders’?: contact tracing in spatio-temporal memory models

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.

A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19

The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used ‘web of Science’, ‘Scopus’ and ‘goggle scholar’ with the keywords of “SARS-CoV-2 detection” or “coronavirus 2019 detection” or “COVID 2019 detection” or “COVID 19 detection” “corona virus techniques for detection of COVID-19”, “audio techniques for detection of COVID-19”, “speech techniques for detection of COVID-19”, for period of 2019–2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single “perfect” COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.

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This is a comprehensive review of published work for the detection of COVID-19.

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Previously conducted studies on audio, voice, cough sound, breathing and signal processing methods in order to address COVID-19-related health conditions.

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Analyzing and diagnosing COVID-19 using audio, speech and Signal Processing.

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Diagnosing and Screening of COVID-19 are studied using Machine Learning, Artificial Intelligence and Deep Learning.

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An overall these Signal Processing, Machine Learning, Artificial Intelligence and Deep Learning techniques were seen to have satisfactory results for the detection of COVID-19.

Development and Validation of a Novel Clinical Prediction Model to Predict the Risk of Lung Metastasis from Ewing Sarcoma for Medical Human-Computer Interface

Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S.

Review on people's trust on home use medical devices during Covid-19 pandemic in India

With the rapid development of the medical device against COVID-19 is an excellent achievement. There are numerous obstacles effectively facing the worldwide population, from manufacture to distribution, deployment and, acceptance. Many manufacturers have entered the market rivalry as people's knowledge and demand for home-use medical equipment has increased. India represents a compelling market opportunity for global medical device manufacturers. Substantial growth for the Indian medical device industry is expected to be driven by the current low per-person spending rate for medical devices. The growth of the medical devices industry in India raises competition law issues (anti-trust) and therefore maintaining public trust in home-use medical devices during COVID-19 will be as essential. The review article aims to create awareness among people about commonly used medical devices during the COVID-19 pandemic and to survey people’s trust in home usable medical devices in India. In a worldwide pandemic, manufacturers of medical devices face insufficient storage and the impossibility of meeting the requirements of the health centre. The sale of some of the most significant medical devices has increased, making it more difficult for the medical device industry to satisfy demand with high-quality goods since the quality of COVID-19 items plays a vital part in the present scenario. Despite the difficulty in providing enough medical equipment during a pandemic, they are striving to adapt to the circumstance. After recognizing the need to promote awareness and grasp the selling, and production, handling of medical instruments during COVID-19 at home was conducted. In addition, medical equipment manufacturers and distributors look at this scenario as an opportunity to profit more. This review article would enable researchers during COVID-19 to build more knowledge and widespread trust in medical technologies respectively.

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In healthcare applications, efficient computation of QoS is one of the mandatory requirements during the processing of medical records through smart measurement methods. Medical services often involve the transmission of demanding information. Thus, there are stringent requirements for secure, intelligent, public-network quality-of-service. This paper contributes to three different aspects. First, we propose a novel metaheuristic approach for medical cost-efficient task schedules, where an intelligent scheduler manages the tasks, such as the rate of service schedule, and lists items utilized by users during the data processing and computation through the fog node. Second, the QoS efficient-computation algorithm, which effectively monitors performance according to the indicator (parameter) with the analysis mechanism of quality-of-experience (QoE), has been developed. Third, a framework of blockchain-distributed technology-enabled QoS (QoS-ledger) computation in healthcare applications is proposed in a permissionless public peer-to-peer (P2P) network, which stores medical processed information in a distributed ledger. We have designed and deployed smart contracts for secure medical-data transmission and processing in serverless peering networks and handled overall node-protected interactions and preserved logs in a blockchain distributed ledger. The simulation result shows that QoS is computed on the blockchain public network with transmission power = average of −10 to −17 dBm, jitter = 34 ms, delay = average of 87 to 95 ms, throughput = 185 bytes, duty cycle = 8%, route of delivery and response back variable. Thus, the proposed QoS-ledger is a potential candidate for the computation of quality-of-service that is not limited to e-healthcare distributed applications.

Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model

As COVID-19 pandemic caused completely spoils the livings, almost more than one year passed, still lives were not on the track. It is important to diagnose the COVID-19 patients earlier and provide the prompt treatment. The Convolutional Neural Network (CNN), a deep neural network that specializes in image processing and image classification. In this paper, a fine tuned DenseNet201 model was proposed which is used to classify Chest X ray images. Firstly, different DenseNet121, DenseNet169 and DenseNet201 model trained and tested on the same dataset. With the experiment, it is observed that DenseNet201 model performs well as compared to other dense models. Furthermore, DenseNet201 experiments over different optimizers and it is noticed that RMSprop, Adagrad and Adamax performs better. Proposed model achieves accuracy of 95.2% as compared to other models. We experimentally determine that RMSprop optimizer with DenseNet201 produces better results as similar to Adam and Adamax widely used optimizers.

Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

Image super-resolution reconstruction for secure data transmission in Internet of Things environment

The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.