Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

BACKGROUND

Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations.

METHOD

This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10-90 and 25-75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants.

RESULT

The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance.

CONCLUSION

Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Evaluating an Advanced Practice Provider-Managed Coronavirus Disease 2019 Deterioration Program

Background

COVID-19 changed how healthcare systems could provide quality healthcare to patients, safely. An urban healthcare system created an advanced practice provider (APP)-managed continuous remote patient monitoring (cRPM) program.

Methods

A mixed-method study design focusing on the usable and feasible nature of the cRPM program. Both APP-guided interviews and online questionnaires were analyzed.

Results

There was overwhelmingly positive APP feedback utilizing the remote monitoring solution including providing quality healthcare, detecting early clinical deterioration, and desiring to adapt the solution to other acute or chronic diseases.

Implications

Understanding the clinical users' feedback on usability and feasibility of cRPM highlights the significance of rapid clinical assessment, urgent care escalation and provider accessibility.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed "environmentally sustainable smart cities." This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991-2015, 2016-2019, and 2020-2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods-due to the acceleration of the digitalization and decarbonization agendas-thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time-some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

[Diagnostic performance of artificial intelligence algorithms for detection of pulmonary involvement by COVID-19 based on portable radiography]

INTRODUCTION AND OBJECTIVES

To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX).

MATERIAL AND METHODS

Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms.

RESULTS

493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)].

CONCLUSION

AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement.

Diagnostic performance of artificial intelligence algorithms for detection of pulmonary involvement by COVID-19 based on portable radiography

Introduction and objectives

To evaluate the diagnostic performance of different artificial intelligence (AI) algorithms for the identification of pulmonary involvement by SARS-CoV-2 based on portable chest radiography (RX).

Material and methods

Prospective observational study that included patients admitted for suspected COVID-19 infection in a university hospital between July and November 2020. The reference standard of pulmonary involvement by SARS-CoV-2 comprised a positive PCR test and low-tract respiratory symptoms.

Results

493 patients were included, 140 (28%) with positive PCR and 32 (7%) with SARS-CoV-2 pneumonia. The AI-B algorithm had the best diagnostic performance (areas under the ROC curve AI-B 0.73, vs. AI-A 0.51, vs. AI-C 0.57). Using a detection threshold greater than 55%, AI-B had greater diagnostic performance than the specialist [(area under the curve of 0.68 (95% CI 0.64-0.72), vs. 0.54 (95% CI 0.49-0.59)].

Conclusion

AI algorithms based on portable RX enabled a diagnostic performance comparable to human assessment for the detection of SARS-CoV-2 lung involvement.

Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field

To address the current pandemic, multiple studies have focused on the development of new mHealth apps to help in curbing the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV-2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. The main objectives of this paper are: (1) Analyze the current status of COVID-19 apps available on the main virtual stores: Google Play Store and App Store for Spain, and (2) Propose a novel mobile application that allows interaction and doctor-patient follow-up without the need for real-time consultations (face-to-face or telephone). In this research, a search for eHealth and telemedicine apps related to Covid-19 was performed in the main online stores: Google Play Store and App Store, until May 2021. Keywords were entered into the search engines of the online stores and relevant apps were selected for study using a PRISMA methodology. For the design and implementation of the proposed app named COVINFO, the main weaknesses of the apps studied were taken into account in order to propose a novel and useful app for healthcare systems. The search yielded a total of 50 apps, of which 24 were relevant to this study, of which 23 are free and 54% are available for Android and iOS operating systems (OS). The proposed app has been developed for mobile devices with Android OS being compatible with Android 4.4 and higher. This app enables doctor-patient interaction and constant monitoring of the patient's progress without the need for calls, chats or face-to-face consultation in real time. This work addresses design and development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of existing applications have this functionality. The COVINFO app offers a novel service: asynchronous doctor-patient communication, as well as constant monitoring of the patient's condition and evolution. This app makes it possible to better manage the time of healthcare personnel and avoid overcrowding in hospitals, with the aim of preventing the collapse of healthcare systems and the spread of the coronavirus.

Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review

Background

The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak.

Methods

This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model.

Results

In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at "on-body," "in-clinic/hospital," and even "in-community" levels.

Conclusions

The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

How Are Material Values and Voluntary Simplicity Lifestyle Related to Attitudes and Intentions toward Commercial Sharing during the COVID-19 Pandemic? Evidence from Japan

This paper attempts to elucidate how material values (MV) and voluntary simplicity lifestyle (VSL) are related to Japanese consumers’ attitudes and intentions toward commercial sharing during the COVID-19 pandemic. This paper provides the following findings by employing the two-step structural equation modeling (SEM) approach to analyze the data (n = 750) collected in Japan during the pandemic from people with no experience in commercial sharing. (1) MV has a positive effect on attitudes. (2) VSL is divided into “simplicity,” “long-term usage,” and “planned buying.” (3) Simplicity and planned buying are negatively related to MV, but long-term usage is not significantly related to MV. (4) Simplicity and long-term usage significantly affect attitudes, whereas planned buying does not. (5) Attitudes and subjective norms have positive effects on intentions. Consequently, two conflicting consumption orientations, MV and VSL, positively affect consumers’ responses toward commercial sharing in a pandemic context. The author suggests that although the negative impact of the COVID-19 pandemic exists now, the sharing economy can still contribute to enhancing sustainability and alleviating technological inequality by attracting people with different values and lifestyles.

Measuring re-identification risk using a synthetic estimator to enable data sharing

Background

One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population.

Objectives

Develop an accurate risk estimator for the sample-to-population attack.

Methods

A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature.

Results

Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset.

Conclusions

The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.

User Perception of New E-Health Challenges: Implications for the Care Process

Establishing new models of health care and new forms of professional health-patient communication are lines of development in the field of health care. The onset of the COVID-19 pandemic has accelerated the evolution of information systems and communication platforms to guarantee continuity of care and compliance with social distancing measures. Our objective in this article was, firstly, to know the expectations of patients treated in the healthcare processes "cervical cancer" and "pregnancy, childbirth and puerperium" regarding online access to their clinical history and follow-up in the care process. Secondly, we analyzed times involved in the cervical cancer process to find points of improvement in waiting times when digital tools were used for communication with the patient. A descriptive cross-sectional study was carried out on 120 women included in any of the aforementioned processes using a hetero-administered questionnaire. The analysis of times was carried out using the Business Intelligence tool Biwer Analytics^®. Patients showed interest in knowing their results before the appointment with the doctor and would avoid appointments with their doctor if the right conditions were met. Most recognized that this action would relieve their restlessness and anxiety. They were highly interested in receiving recommendations to improve their health status. It was estimated that there was room for improvement in the times involved in the care process, which could be shortened by 34.48 days if communication of results were through digital information access technologies. This would favor the optimization of time, resources and user perception.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review

Purpose

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

Design/methodology/approach

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

Findings

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research limitations/implications

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

Practical implications

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

Originality/value

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Application of artificial intelligence in COVID-19 medical area: a systematic review

Background

Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.

Methods

We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.

Results

We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.

Discussion

This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians’ role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.

Predicting the Classification of Home Oxygen Therapy for Post-COVID-19 Rehabilitation Patients Using a Neural Network

OBJECTIVE

We evaluated the accuracy of a neural network to classify and predict the possibility of home oxygen therapy at the time of discharge from hospital based on patient information post-coronavirus disease (COVID-19) at admission.

METHODS

Patients who survived acute treatment with COVID-19 and were admitted to the Amagasaki Medical Co-operative Hospital during August 2020-December 2021 were included. However, only rehabilitation patients (n = 88) who were discharged after a rehabilitation period of at least 2 weeks and not via home or institution were included. The neural network model implemented in R for Windows (4.1.2) was trained using data on patient age, gender, and number of days between a positive polymerase chain reaction test and hospitalization, length of hospital stay, oxygen flow rate required at hospitalization, and ability to perform activities of daily living. The number of training trials was 100. We used the area under the curve (AUC), accuracy, sensitivity, and specificity as evaluation indicators for the classification model.

RESULTS

The model of states at rest had as AUC of 0.82, sensitivity of 75.0%, specificity of 88.9%, and model accuracy of 86.4%. The model of states on exertion had an ACU of 0.82, sensitivity of 83.3%, specificity of 81.3%, and model accuracy of 81.8%.

CONCLUSION

The accuracy of this study's neural network model is comparable to that of previous studies recommended by Japanese Guidelines for the Physical Therapy and is expected to be used in clinical practice. In future, it could be used as a more accurate clinical support tool by increasing the sample size and applying cross-validation.

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

Bio-safety and bio-security: A major global concern for ongoing COVID-19 pandemic

Besides its impacts on governance, economics, human culture, geostrategic partnership and environment, globalization greatly exerted control over science and security policies. Biosecurity is the critical job of efforts, policy and preparation to protect health of human, animal and environmental against any biological threats. With the transition into a global village, the possibility of biosecurity breaches has significantly increased. The COVID-19 pandemic is an example of an infringement on biosecurity that has posed a serious threat to the world. Since the first report on the recognition of COVID-19, a number of governments have taken preventive measures, like; lockdown, screening and early detection of suspected and implementing the required response to protect the loss of life and economy. Unfortunately, some of these measures have only recently been taken in some countries, which have contributed significantly to an increased morbidity and loss of life on a daily basis. In this article, the biological risks affecting human, animal and environmental conditions, biosafety violations and preventive measures have been discussed in order to reduce the outbreak and impacts of a pandemic like COVID-19.

Impact of Covid-19 on research and training in Parkinson's disease

The Coronavirus Disease 2019 (Covid-19) pandemic and the consequent restrictions imposed worldwide have posed an unprecedented challenge to research and training in Parkinson's disease (PD). The pandemic has caused loss of productivity, reduced access to funding, an oft-acute switch to digital platforms, and changes in daily work protocols, or even redeployment. Frequently, clinical and research appointments were suspended or changed as a solution to limit the risk of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) spread and infection, but since the care and research in the field of movement disorders had traditionally been performed at in-person settings, the repercussions of the pandemic have even been more keenly felt in these areas. In this chapter, we review the implications of this impact on neurological research and training, with an emphasis on PD, as well as highlight lessons that can be learnt from how the Covid-19 pandemic has been managed in terms of restrictions in these crucial aspects of the neurosciences. One of the solutions brought to the fore has been to replace the traditional way of performing research and training with remote, and therefore socially distanced, alternatives. However, this has introduced fresh challenges in international collaboration, contingency planning, study prioritization, safety precautions, artificial intelligence, and various forms of digital technology. Nonetheless, in the long-term, these strategies will allow us to mitigate the adverse impact on PD research and training in future crises.

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system.

An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered.

The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period.

OntoRepliCov: an Ontology-Based Approach for Modeling the SARS-CoV-2 Replication Process

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

Management of Parkinson's Disease in the COVID-19 Pandemic and Future Perspectives in the Era of Vaccination

The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a serious global health crisis. Increasing evidence suggests that elderly individuals with underlying chronic diseases, including Parkinson's disease (PD), are particularly vulnerable to this infection. Changes in the routine care of PD patients should be implemented carefully without affecting the quality provided. The utilization of telemedicine for clinical consultation, assessment and rehabilitation has also been widely recommended. Therefore, the aim of this review is to provide recommendations in the management of PD during the pandemic as well as in the early phase of vaccination programs to highlight the potential sequelae and future perspectives of vaccination and further research in PD. Even though a year has passed since COVID- 19 emerged, most of us are still facing great challenges in providing a continuum of care to patients with chronic neurological disorders. However, we should regard this health crisis as an opportunity to change our routine approach in managing PD patients and learn more about the impact of SARS-CoV-2. Hopefully, PD patients can be vaccinated promptly, and more detailed research related to PD in COVID-19 can still be carried out.

The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory

BACKGROUND

Machine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians' practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians' competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician-patient relationship might be affected by the employment of ML_CDSS.

MAIN TEXT

Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient's concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.

CONCLUSIONS

Using ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient's personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient's concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view

Since December 2019, we have been in the battlefield with a new threat to the humanity known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we describe the four main methods used for diagnosis, screening and/or surveillance of SARS-CoV-2: Real-time reverse transcription polymerase chain reaction (RT-PCR); chest computed tomography (CT); and different complementary alternatives developed in order to obtain rapid results, antigen and antibody detection. All of them compare the highlighting advantages and disadvantages from an analytical point of view. The gold standard method in terms of sensitivity and specificity is the RT-PCR. The different modifications propose to make it more rapid and applicable at point of care (POC) are also presented and discussed. CT images are limited to central hospitals. However, being combined with RT-PCR is the most robust and accurate way to confirm COVID-19 infection. Antibody tests, although unable to provide reliable results on the status of the infection, are suitable for carrying out maximum screening of the population in order to know the immune capacity. More recently, antigen tests, less sensitive than RT-PCR, have been authorized to determine in a quicker way whether the patient is infected at the time of analysis and without the need of specific instruments.

Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management

The World Health Organization (WHO) has declared the COVID-19 an international health emergency due to the severity of infection progression, which became more severe due to its continuous spread globally and the unavailability of appropriate therapy and diagnostics systems. Thus, there is a need for efficient devices to detect SARS-CoV-2 infection at an early stage. Nowadays, the reverse transcription polymerase chain reaction (RT-PCR) technique is being applied for detecting this virus around the globe; however, factors such as stringent expertise, long diagnostic times, invasive and painful screening, and high costs have restricted the use of RT-PCR methods for rapid diagnostics. Therefore, the development of cost-effective, portable, sensitive, prompt and selective sensing systems to detect SARS-CoV-2 in biofluids at fM/pM/nM concentrations would be a breakthrough in diagnostics. Immunosensors that show increased specificity and sensitivity are considerably fast and do not imply costly reagents or instruments, reducing the cost for COVID-19 detection. The current developments in immunosensors perhaps signify the most significant opportunity for a rapid assay to detect COVID-19, without the need of highly skilled professionals and specialized tools to interpret results. Artificial intelligence (AI) and the Internet of Medical Things (IoMT) can also be equipped with this immunosensing approach to investigate useful networking through database management, sharing, and analytics to prevent and manage COVID-19. Herein, we represent the collective concepts of biomarker-based immunosensors along with AI and IoMT as smart sensing strategies with bioinformatics approach to monitor non-invasive early stage SARS-CoV-2 development, with fast point-of-care (POC) diagnostics as the crucial goal. This approach should be implemented quickly and verified practicality for clinical samples before being set in the present times for mass-diagnostic research.

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.

A novel approach utilizing laser acupuncture teletherapy for management of elderly-onset rheumatoid arthritis: A randomized clinical trial

INTRODUCTION

Rheumatoid arthritis (RA) disease is a systemic progressive inflammatory autoimmune disorder. Elderly-onset RA can be assumed as a benign form of RA. Until recently, face-to-face therapeutic sessions between health professionals and patients are usually the method of its treatment. However, during pandemics, including coronavirus disease 2019 (COVID-19), teletherapeutic sessions can extensively increase the patient safety especially in elderly patients who are more vulnerable to these infections. Thus, the aim of this randomized clinical trial was to evaluate a novel teletherapy approach for management of elderly patients suffering from RA by utilizing laser acupuncture.

METHODS

A teletherapy system was used for management of elderly patients suffering from RA. Sixty participants were allocated randomly into two groups and the ratio was 1:1. Patients in the first group were treated with laser acupuncture and telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Patients in the second group received telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Evaluation of patients was done by using the Health Assessment questionnaire (HAQ), the Rheumatoid Arthritis Quality of Life (RAQoL) questionnaire, and the analysis of interleukin-6 (IL-6), serum C-reactive protein (CRP), plasma adenosine triphosphate (ATP) concentration and plasma malondialdehyde (MDA).

RESULTS

A statistically significant difference was found in CRP, RAQoL, IL-6 and MDA between the pre- and post-treatments in the first group (p < 0.05) favouring the post-treatment group, while the HAQ showed a statistically significant difference between pre- and post-treatments (p < 0.05) in both groups. Statistically significant post-treatment differences were also observed between the two groups (p < 0.05) in RAQoL, CRP, ATP and MDA, favouring the first group.

DISCUSSION

Laser acupuncture teletherapy could be suggested as a reliable treatment method for elderly patients suffering from RA, as it can provide a safe and effective therapeutic approach. Teletherapy provided safer access to health professionals and patients while giving a high patient satisfaction value with a relatively lower cost (ClinicalTrials.gov Identifier: NCT04684693).

Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions.

The Challenge of Reorganizing Rehabilitation Services at the Time of COVID-19 Pandemic: A New Digital and Artificial Intelligence Platform to Support Team Work in Planning and Delivering Safe and High Quality Care

Introduction: The COVID-19 pandemic has posed great challenges in inpatient rehabilitation services, not only to implement the preventative measures to avoid the spreading of the virus in a highly interactive, multidisciplinary setting but also to create a rehabilitation pathway for post-COVID-19 patients. The aim of this retrospective study was to describe the role of a digital and artificial intelligence platform (DAIP) in facilitating the implementation of changes in a rehabilitation service during the COVID-19 pandemic. Materials and Methods: We gathered qualitative and quantitative descriptors of the DAIP, including measures to assess its efficiency in scheduling therapy sessions, and staff satisfaction using two simple numeric rating scales and the System Usability Scale. We describe how the volume of activity and the quality of care of our rehabilitation service have changed when the DAIP was implemented by comparing the pre-COVID-19 and the pandemic periods for patients' [sex, age, co-morbidities, diagnosis, and Functional Independence Measure (FIM) gain] and service's (bed occupancy, patients' length of stay, and staff capacity) characteristics. Results: Bed occupancy and the impact of rehabilitation on patients' outcome remained stable between the two periods. The DAIP provided a qualitative support for goal setting from remote; 95% of the planned sessions were delivered; the time for scheduling and registering sessions dropped by 50%. Staff satisfaction was about 70% for the easiness and 60% for the usefulness, and the mean "usability" score was close to the cut off for sufficient usability (mean score 65 where 68 is the cut off). Conclusion: By applying the DAIP to rehabilitation treatment, it was shown that the management of rehabilitation can be efficiently performed even in the COVID-19 pandemic. Staff satisfaction reflected a good acceptance of the changes considering the turbulent changes and the stress burden occurring at the time of the pandemic.

Telemanagement of Home-Isolated COVID-19 Patients Using Oxygen Therapy With Noninvasive Positive Pressure Ventilation and Physical Therapy Techniques: Randomized Clinical Trial

Background

With the growing stress on hospitals caused by the COVID-19 pandemic, the need for home-based solutions has become a necessity to support these overwhelmed hospitals.

Objective

The goal of this study was to compare two nonpharmacological respiratory treatment methods for home-isolated COVID-19 patients using a newly developed telemanagement health care system.

Methods

In this single-blinded randomized clinical trial, 60 patients with stage 1 pneumonia caused by SARS-CoV-2 infection were treated. Group A (n=30) received oxygen therapy with bilevel positive airway pressure (BiPAP) ventilation, and Group B (n=30) received osteopathic manipulative respiratory and physical therapy techniques. Arterial blood gases of PaO₂ and PaCO₂, pH, vital signs (ie, temperature, respiratory rate, oxygen saturation, heart rate, and blood pressure), and chest computed tomography scans were used for follow-up and for assessment of the course and duration of recovery.

Results

Analysis of the results showed a significant difference between the two groups (P<.05), with Group A showing shorter recovery periods than Group B (mean 14.9, SD 1.7 days, and mean 23.9, SD 2.3 days, respectively). Significant differences were also observed between baseline and final readings in all of the outcome measures in both groups (P<.05). Regarding posttreatment satisfaction with our proposed telemanagement health care system, positive responses were given by most of the patients in both groups.

Conclusions

It was found that home-based oxygen therapy with BiPAP can be a more effective prophylactic treatment approach than osteopathic manipulative respiratory and physical therapy techniques, as it can impede exacerbation of early-stage COVID-19 pneumonia. Telemanagement health care systems are promising methods to help in the pandemic-related shortage of hospital beds, as they showed reasonable effectiveness and reliability in the monitoring and management of patients with early-stage COVID-19 pneumonia.

Trial Registration

ClinicalTrials.gov NCT04368923; https://clinicaltrials.gov/ct2/show/NCT04368923

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

OBJECTIVE

Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs.

METHODS

Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1-5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score.

RESULTS

All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively.

CONCLUSION

Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019.

ADVANCES IN KNOWLEDGE

Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

BACKGROUND/OBJECTIVES

The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions.

METHODS

Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic.

RESULTS

Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them.

CONCLUSIONS

In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.

Effects of laser acupuncture tele-therapy for rheumatoid arthritis elderly patients

Rheumatoid arthritis (RA) is a progressive common autoimmune disorder and is one of the most functional limiting diseases in elderly. Until recently, its treatment is mainly based on physical locations and meetings while being face to face. However, laser acupuncture tele-therapy approaches can significantly provide the patient with safety during the COVID-19 pandemic as well as changing the disorder’s prognosis. Sixty patients were assigned randomly into 2 groups with 1:1 ratio. Patients in group A are treated remotely by laser acupuncture in addition to methotrexate and a tele-rehabilitation program in the form of aerobic exercise training. Patients in group B are treated by methotrexate and a tele-rehabilitation program in the form of aerobic exercise. There was a statistically significant difference in health assessment questionnaire (HAQ) pre- and post-treatment in group A (p < 0.05). The C-reactive protein (CRP) and interleukin-6 (IL-6) inflammatory markers as well as the malondialdehyde (MDA) oxidative marker showed a significant reduction pre- and post-treatment in group A (p < 0.05). Additionally, there was a significant increase in the adenosine tri-phosphate (ATP) antioxidant marker pre- and post-treatment in group A (p < 0.05). The comparison between groups A and B showed a statistically significant post-treatment difference in RAQoL, CRP, IL-6, ATP, and MDA in group A than group B. Considering the significant improvement that was found in the laser acupuncture group, it can be concluded that the use of laser acupuncture as adjunctive was effective in the treatment of elderly patients with RA. ClinicalTrials.gov Identifier: NCT04758689

A REVIEW ON EMERGING PATHOGENESIS OF COVID-19 AND POINTS OF CONCERN FOR RESEARCH COMMUNITIES IN NIGERIA

BACKGROUND

COVID-19 remains an emerging pandemic that continuously poses an alarming threat and challenge to economic, social and well-being of the people throughout the world. It also remains an evolving disease which complete pathogenesis that translates into clinical features is only just emerging by each second of the day. There have been observations about the emerging trends of the disease in Nigeria like in any other country in the world where there is outbreak. This study examined from evidence-based literature the emerging pathogenesis of COVID-19 and important points of concern of the disease in Nigeria.

MATERIALS AND METHODS

The paper reviewed published articles in PubMed and Google Scholar using search terms 'COVID-19" and "SARS-CoV-2", as well as searched for general COVID-19 information on internet.

RESULTS

The result summarized literature on emerging pathogenesis of COVID-19 and important points of concern as well as research questions as to the peculiar trends of the disease in Nigeria.

CONCLUSION

Pathogenesis of COVID-19 remains an emerging knowledge and there are many important research questions that need to be scientifically answered for a successful containment of COVID-19 in Nigeria. It is recommended that all members of intellectual research communities should join the fight against COVID-19 pandemic.