During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely ...utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors’ implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond.
•State-of-the-art Literature Categorization for Telehealth utilization during COVID-19.•Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19.•Different Applications of Telehealth during the COVID-19 pandemic.
Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe ...acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.
Abstract
Today’s healthcare system relies heavily on medical imaging to carry out non-invasive diagnostic treatments. For clinical analysis, it entails the development of graphical and functional ...models of the human body and organs. In order to diagnose non-small cell lung cancer, the Multi-resolution patch (MSP) approach is introduced in this study. The model can enhance detection performance by utilizing an atrous convolution network, however caution must be used while selecting the atrous rate. More data from many institutions is needed to improve the generalization of the model because the validation CT data was only collected at one center; this dataset included lung CT imaging data from healthy individuals. The outcome indicates that the model’s performance can be further enhanced by include data from healthy individuals in the training process. Additionally, two sets of experiments show the value of the pre-processing module and the superiority of the suggested network.
Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains ...highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains.
Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
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•Application of sensors and artificial intelligence to analyse ankle biomechanics.•Lab sensors predict parameters, paving the way for post-modern rehabilitation.•Field devices show potential for ankle injury prediction.•Focus on streamlined smart sensors and effective data management in the future.
Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is ...difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.
Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in ...many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence ...of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.
Numerous studies have focused on making telemedicine smart through the Internet of Things (IoT) technology. These works span a wide range of research areas to enhance telemedicine architecture such ...as network communications, artificial intelligence methods and techniques, IoT wearable sensors and hardware devices, smartphones and cloud computing. Accordingly, several telemedicine applications covering various human diseases have presented their works from a specific perspective and resulted in confusion regarding the IoT characteristics. Although such applications are useful and necessary for improving telemedicine contexts related to monitoring, detection and diagnostics, deriving an overall picture of how IoT characteristics are currently integrated with the telemedicine architecture is difficult. Accordingly, this study complements the academic literature with a systematic review covering all main aspects of advances in IoT-based telemedicine architecture. This study also provides a state-of-the-art telemedicine classification taxonomy under IoT and reviews works in different fields in relation to that classification. To this end, this study checked the ScienceDirect, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Web of Science databases. A total of 2121 papers were collected from 2014 to July 2020. The retrieved articles were filtered according to the defined inclusion criteria. A final set of 141 articles were selected and classified into two categories, each followed by subcategories and sections. The first category includes an IoT-based telemedicine network that accounts for 24.11% (n = 34/141). The second category includes IoT-based telemedicine healthcare services and applications that account for 75.89% (n = 107/141). This multi-field systematic review has exposed new research opportunities, motivations, recommendations and challenges that need attention for the synergistic integration of interdisciplinary works. This extensive study also lists a set of open issues and provides innovative key solutions along with a systematic review. The classification of diseases under IoT-based telemedicine is divided into 14 groups. Furthermore, the crossover in our taxonomy is demonstrated. The lifecycle of the context of IoT-based telemedicine healthcare applications is mapped for the first time, including the procedure sequencing and definition for each context. We believe that this study is a useful guide for researchers and practitioners in providing direction and valuable information for future research. This study can also address the ambiguity in the trends in IoT-based telemedicine.
•Systematic review of IoT-based telemedicine enabled for disease prevention and health promotion is presented.•Mapping the research of IoT- based telemedicine topology along with its platform and architecture into a coherent taxonomy.•Crossover among telemedicine healthcare services/applications and human diseases under IoT is presented.•Figure out the motivations, challenges and recommendations of using IoT-based telemedicine into various categories.•Several issues and innovative key solutions surrounding IoT-based telemedicine are explored.
Extensive research has been conducted on e-tourism spanning a wide range of concepts, challenges and concerns discussed in tourism recommender systems (TRS). Smart tourism can be considered a logical ...progression from e-tourism laid with the extensive adoption of information and communication technologies and connecting the physical and digital worlds by taking advantage of 12 ‘smart key concepts’ such as privacy protection, Internet of Things and augmented reality, among others. Consequently, several disparate types of research have existed in various classes of TRS that have accomplished smart key concepts where others have failed. However, such piecemeal development is insufficient for a pragmatic smart tourism solution. Accordingly, the current study complements the academic literature with a systematic review that covers all main aspects of the e-tourism management system applied to the smart tourism concepts over the last eight years of publication. This study also provides a state-of-the-art e-tourism data management classification taxonomy based on smart concepts and reviews works in different fields against that classification. To this end, we reviewed the ScienceDirect, IEEE Xplore and Web of Science databases. A total of 1240 papers were collected from 2013 to 2020. The retrieved articles were filtered according to the defined inclusion criteria. Finally, 65 articles were selected and classified into two categories. The first category includes smart-based TRS that accounts for 87.70% (n = 57/65) and classified into four approaches: collaborative filtering, content model, context model and hybrid model. The second category includes tourism marketing that accounts for 12.30% (n = 8/65). This multi-field systematic review has exposed new research opportunities, motivations, recommendations and challenges and limitations that need attention for the synergistic smart integration of interdisciplinary studies. The reliability and acceptability of smart-based TRS approach from the implemented 12 smart key concepts show a significant difference. Analysis shows that the content model-based approach has a highly important effect on smart e-tourism, i.e., applying numerous smart key concepts in higher mean (40.2%). Results of several past studies that used a content model-based approach were nearly perceived as smart e-tourism. The smartly achieved key concepts for hybrid and context-based approaches have approximate means of (37.9%) and 36.6%, respectively, thereby confirming results. The results of tourism marketing and collaborative filtering approaches are worse than the previously reported results, achieving means of (33.3%) and (30.3%), respectively. This study is a useful guide for researchers and practitioners in providing avenues and valuable information for future research. This study is also expected to address the ambiguity of e-tourism and smart tourism trends.
Three-dimensional (3D) technique of restricting scrambling is changing the ways of drug design, labeling and production in the area of digital health. By combining digital and genetic techniques, ...Fused Deposition Modeling (FDM) can manufacture normalization systems. Consecutively, such a method can allow for speedy improvements in the healthcare systems, allowing the allocation of medicines based on patient's needs and requirements. So far, several 3D based medicinal goods have been marketed. These include the production of implants and several useful related products for use in medical applications. Nevertheless, regulatory obstacles remain with developing medicines. This article reviews the latest FDM technology in medical and pharmaceutical research, including a discussion of the potential challenges in the field. Emphasis has been paid on future developments needed for facilitating the FDM integration into dispensaries and clinics.
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