The Vocal Hygiene Treatment Programs for Singers: A Systematic Review.

OBJECTIVES: This systematic review aims to explore the effectiveness of voice health education interventions among singers, particularly focusing on vocal hygiene treatment programs tailored for professional voice users. STUDY DESIGN: Systematic review. METHODS: Preferred Reporting Items on Systematic Reviews and Meta-Analysis guidelines were followed to conduct this systematic review. Comprehensive searches were conducted in PubMed, Web of Science, Scopus, Science Direct, and Cochrane Library databases. Four articles were selected for detailed review. The studies were evaluated using the Effective Public Health Practice Project tool for quality assessment. RESULTS: The four reviewed studies primarily utilized the pretest-posttest design to examine the effectiveness of vocal hygiene interventions on singers' vocal health. Two studies investigated the effect of hydration as a treatment method, while the remaining two focused on vocal hygiene instruction. Significant improvements were observed in various vocal health parameters, including maximum phonation time, intensity, Dysphonia Severity Index, and number of daily vocal breaks taken. CONCLUSION: This systematic review provides valuable insights into the efficacy of vocal hygiene treatment programs for singers. The positive outcomes observed in the reviewed studies underscore the importance of voice health education tailored to singers' specific needs. However, the limited number of eligible studies and the common limitation of small sample sizes highlight the need for further research in this area. Vocal health practitioners, educators, and researchers can utilize the findings of this review to develop evidence-based vocal hygiene interventions that promote the well-being and longevity of singers' vocal performance careers.

Adaptation and validation of the Persian Version of the Singing Voice Handicap Index-10 (SVHI-10).

OBJECTIVES: Singers' self-reporting of their voice problems can be helpful in the treatment of voice disorders by speech-language pathologists (SLP), which requires a valid questionnaire. This study was conducted to translate and validate the Singing Voice Handicap Index-10 (SVHI-10) questionnaire in the Persian language. METHODS: This cross-sectional study was conducted in two main steps (1) translation of the questionnaire and determination of face and content validity and (2) survey of singers. The questionnaire was first translated by a SLP who was an expert in voice disorder and also proficient in the English language. The face and content validity of the questionnaire was confirmed by experts through impact score, content validity ratio, and content validity index. Then it was translated into English and adapted to its original version. A valid questionnaire was given to 70 singers to determine their ability to distinguish singers with voice problems from healthy singers and their internal consistency. RESULTS: The face and content validity of the questionnaire was confirmed without excluding any item. The results showed that the questionnaire has excellent internal consistency (α = 0.930). With the optimal cut-off point of 14.5, this questionnaire was able to identify singers with voice problems with 90% accuracy. Also, the sensitivity and specificity were 84.85% and 94.59%, respectively. Also, the area under the Receiver Operating Characteristic (ROC) curve was equal to 0.937. CONCLUSION: The results revealed that the Persian version of SVHI-10 is a reliable and valid instrument to identify singers with voice problems, so it can be used by SLPs.

Virtual Reality and Cardiac Diseases: A Systematic Review of Applications and Effects.

Introduction: Cardiac diseases have grown significantly in recent years, causing many deaths globally. Cardiac diseases can impose a significant economic burden on societies. The development of virtual reality technology has attracted the attention of many researchers in recent years. This study aimed to investigate the applications and effects of virtual reality (VR) technology on cardiac diseases. Methods: A comprehensive search was carried out in four databases, including Scopus, Medline (through PubMed), Web of Science, and IEEE Xplore to identify related articles published until May 25, 2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guideline for systematic reviews was followed. All randomized trials that investigated the effects of virtual reality on cardiac diseases were included in this systematic review. Results: Twenty-six studies were included in this systematic review. The results illustrated that virtual reality applications in cardiac diseases can be classified in three categories of physical rehabilitation, psychological rehabilitation, and education/training. This study revealed that the use of virtual reality in psychological and physical rehabilitation can reduce stress, emotional tension, Hospital Anxiety and Depression Scale (HADS) total score, anxiety, depression, pain, systolic blood pressure, and length of hospitalization. Finally, the use of virtual reality in education/training can enhance technical performance, increase the speed of procedures, and improve the user's skills, level of knowledge, and self-confidence as well as facilitate learning. Also, the most limitations mentioned in the studies included small sample size and lack of or short duration of follow-up. Conclusions: The results showed that the positive effects of using virtual reality in cardiac diseases are much more than its negative effects. Considering that the most limitations mentioned in the studies were the small sample size and short duration of follow-up, it is necessary to conduct studies with adequate methodological quality to report their effects in the short term and long term.

The willingness and attitudes of speech-language pathologists towards the use of mobile health technology: a survey study.

BACKGROUND: Mobile health (mHealth) technology could be used in different ways to treat various speech and language disorders. The attitude of speech-language pathologists (SLPs) towards this technology and their willingness to use it can play a significant role in the success of the therapies they provide. This study was conducted to investigate the willingness and attitude of SLPs towards the use of mHealth technology. METHODS: This cross-sectional study was conducted from September 2021 to April 2022 in Iran. A researcher-made questionnaire consisting of three parts (information related to demographic variables, attitude and willingness) was designed based on the past studies, and then given to all SLPs throughout Iran. Data were analyzed by SPSS software, using descriptive and inferential statistics (frequency, mean, Fisher's exact test, and analysis of variance). Also, the SLPs' willingness to use the desired technology was interpreted as a percentage as follows: 0-20% = not at all willing, 21-40% = slightly willing, 41-60% = moderately willing, 61-80% = highly willing, and above 80% = extremely willing. RESULTS: One hundred sixty speech-language pathologists from all over Iran participated in this study. The results showed that the willingness of 65.25% of SLPs to use the mentioned technology was at a good level, and according to the mentioned category, they had a high willingness to use this technology. In regard to the attitude of SLPs, the findings showed that SLPs believed that patients receive a higher quality of care during in-person visits than through mHealth technology. Also, this survey showed that SLPs were more inclined to use this technology to answer patients' questions. Non-payment of services provided through mHealth technology and privacy concerns were the reasons for the lack of use of this technology by SLPs. CONCLUSIONS: SLPs are willing to use mHealth technology after solving the related challenges, including payment of costs and privacy concerns. However, SLPs believed that this technology will not be a suitable alternative to face-to-face sessions.

Opportunities and challenges of virtual reality-based interventions for patients with breast cancer: a systematic review.

BACKGROUND: Breast cancer is one of the most common cancers diagnosed worldwide and the second leading cause of death among women. Virtual reality (VR) has many opportunities and challenges for breast cancer patients' rehabilitation and symptom management. The purpose of this systematic review is to look into the benefits and drawbacks of VR interventions for breast cancer patients. METHODS: A systematic search was conducted on PubMed, Web of Science, Scopus, IEEE, and the Cochrane Library, from inception until February 6, 2022. The inclusion criteria were: (1) original studies without restriction in study design; (2) a study population consisting of patients with breast cancer; (3) any type of VR-based interventions (immersive and non-immersive); and (5) studies published in English. To assess the risk of bias, the Effective Public Health Practice Project (EPHPP) Tool was used. RESULTS: Eighteen articles were included in this systematic review. The result showed that VR could provide many opportunities for patients with breast cancer, including reducing anxiety, time perception, pain, fatigue, chemotherapy-related symptom distress levels, and depression severity, as well as improvement in the range of motion, strength, and function. Cybersickness symptoms, the weight of headsets and helmets, the quality of the visual image, and the cost of the equipment are some of the challenges in using this technology on these patients. CONCLUSIONS: The systematic review showed that VR interventions have opportunities and challenges for patients with breast cancer. VR can be effective for rehabilitation and symptom management and is used in different stages of treatment to improve the condition of patients with breast cancer. However, before using it, the researcher should consider its challenges.

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. MATERIALS AND METHODS: A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. RESULTS: The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05). CONCLUSION: The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.

An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

BACKGROUND: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. OBJECTIVE: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. METHOD: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. RESULTS: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. CONCLUSION: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public.

The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review.

Wearable smart sensors are emerging technology for daily monitoring of vital signs with the reducing discomfort and interference with normal human activities. The main objective of this study was to review the applied wearable smart sensors for disease control and vital signs monitoring in epidemics outbreaks. A comprehensive search was conducted in Web of Science, Scopus, IEEE Library, PubMed and Google Scholar databases to identify relevant studies published until June 2, 2020. Main extracted specifications for each paper are publication details, type of sensor, disease, type of monitored vital sign, function and usage. Of 277 articles, 11 studies were eligible for criteria. 36% of papers were published in 2020. Articles were published in 10 different journals and only in the Journal of Medical Systems more than one article was published. Most sensors were used to monitor body temperature, heart rate and blood pressure. Wearable devices (like a helmet, watch, or cuff) and body area network sensors were popular types which can be used monitoring vital signs for epidemic trending. 65% of total papers (n = 6) were conducted by the USA, Malaysia and India. Applying appropriate technological solutions could improve control and management of epidemic disease as well as the application of sensors for continuous monitoring of vital signs. However, further studies are needed to investigate the real effects of these sensors and their effectiveness.

Medical Informaticians' Skills and Competencies to Achieve Roles and Opportunities: A Qualitative Study.

For Medical Informatics graduates, there is no compatibility between the training knowledge and skills at universities and the job requirements. This study aimed to determine the skills and competencies requirements for medical informatics graduates and possible job positions in an emerging discipline. This qualitative study was conducted using a questionnaire developed by the researchers. Nine independent medical informatics professionals assessed the initial draft of this tool to determine its face and content validity, and reliability. The questionnaire was distributed among 80 medical informaticians with a doctorate or a master's degree. In this study, items with an average of 4 and higher were confirmed; out of the 78 items, 66 were approved. The highest number of unapproved items was related to managerial knowledge and skills. Research knowledge, training skills, individual skills, technical capacities, specific skills in the health industry, and managerial skills are the main areas that graduates must learn. This survey can help develop a curriculum and job descriptions for medical informatics.

Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Evaluating the usability of a national health information system with heuristic method.

BACKGROUND: Hospital Statistics and Information System is one of the most important health information systems in Iran used in all hospitals in this country. Usability problems can reduce the speed and precision of users when interacting with this system. This study aimed to identify the usability problems of a national health system called "AVAB". MATERIALS AND METHODS: This descriptive cross-sectional study was conducted in 2020, and three experts evaluated the usability of this system independently by the heuristic evaluation method. Nielsen's usability principles were used to identify usability problems and to classify their severity. RESULTS: A total of 86 unique problems were identified. The highest number of problems were related to the two principles of "help and documentation" and "match between system and the real world" with 23 and 11 usability problems, respectively. The lowest number of problems were related to the two principles of "visibility of system status" and "help users recognize, diagnose, and recover from errors," each with three problems. 58.1% of the identified problems were in the group of major and catastrophic problems. CONCLUSIONS: With the help of heuristic evaluation method, a significant number of usability problems of Hospital Statistics and Information System were identified. Most of the identified problems were major and catastrophic, and it is necessary to solve these problems by the designers and developers of this system.

Identifying a minimum data set as a necessity to design a web-based personal health record for patients under chronic dialysis.

Objective: Many people suffer from kidney disease, and self-management is essential in these patients. Personal health record (PHR) can be used as a tool to improve self-management in these patients. This study aimed to identify a minimum data set (MDS) of PHR in dialysis patients. Methods: This descriptive and cross-sectional study was conducted in 2019, and national and international scientific literature entitled "Personal Health Record," "Electronic Personal Health Record," "Dialysis Patient Portal," "Dialysis Health Record," and "Dialysis Information Needs" by content analysis method was reviewed. A questionnaire with 14 items was designed to examine patients' problems and data needs based on the review of scientific literature and web-based PHRs. Based on the patients' survey and the review of scientific literature, a questionnaire with 114 questions was designed. Finally, with experts' opinions, data elements were determined. Results: An MDS for developing web-based PHR for patients under chronic dialysis was created with 17 data classes including demographic information, insurance information, contact information in case of emergency, information on dialysis sessions, physicians information, dialysis center information, information on individual measured values (blood pressure, blood sugar, and weight), disease history information, information on surgical procedures and operations, history of visits, allergies, vaccinations, family history, drugs, laboratory tests, diet, and education materials for the patient. Conclusion: In this study, an MDS was developed for a web-based PHR for dialysis patients. The use of standard data can help collect the data that is essential to improve the patient's health and track his medical condition.

Application of Digital Games for Speech Therapy in Children: A Systematic Review of Features and Challenges.

Introduction: Treatment of speech disorders during childhood is essential. Many technologies can help speech and language pathologists (SLPs) to practice speech skills, one of which is digital games. This study aimed to systematically investigate the games developed to treat speech disorders and their challenges in children. Methods: A comprehensive search was conducted in four databases, including Medline (through PubMed), Scopus, Web of Science, and IEEE Xplore, to retrieve English articles published by July 14, 2021. The articles in which a digital game was developed to treat speech disorders in children were included in the study. Then, the features of the designed games and their challenges were extracted from the studies. Results: After reviewing the full texts of 69 articles and assessing them in terms of inclusion and exclusion criteria, 27 articles were included in the systematic review. In these articles, 59.25% of the games had been developed in English language and children with hearing impairments had received much attention from researchers compared to other patients. Also, the Mel-Frequency Cepstral Coefficients (MFCC) algorithm and the PocketSphinx speech recognition engine had been used more than any other speech recognition algorithm and tool. In terms of the games, 48.15% had been designed in a way that children could practice with the help of their parents. The evaluation of games showed a positive effect on children's satisfaction, motivation, and attention during speech therapy exercises. The biggest barriers and challenges mentioned in the studies included sense of frustration, low self-esteem after several failures in playing games, environmental noise, contradiction between games levels and the target group's needs, and problems related to speech recognition. Conclusion: The results of this study showed that the games positively affect children's motivation to continue speech therapy, and they can also be used as the SLPs' aids. Before designing these tools, the obstacles and challenges should be considered, and also, the solutions should be suggested.

Telemedicine for Patient Management in Remote Areas and Underserved Populations.

Access to care services in remote areas is challenging. The use of telemedicine technology in these areas facilitates access to health care. This study aimed to summarize the current research on telemedicine in remote areas such as mountains and forests. A systematic search was conducted in databases including Medline (through PubMed), Scopus, IEEE Xplore Digital Library, and ISI Web of Science to identify relevant studies published until May 12, 2021. Screening of retrieved articles for selection and inclusion in the study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes extension for Scoping Reviews (PRISMA-ScR) checklist. A total of 807 articles were identified after removing duplicates, from which 20 studies meeting our inclusion criteria were selected. Challenges, opportunities, and equipment required to use telemedicine in remote areas were extracted from the selected studies. The results revealed that telemedicine implementation in remote areas had many challenges, including harsh weather conditions, Internet connectivity problems, difficult equipment transportation, and ethical issues. Telemedicine also has many benefits, such as cost and time savings for patients, improving patients' quality of life, and improving patient satisfaction. Telemedicine for inhabitants of forested and mountainous areas facilitates rapid access to health care and enhances patient satisfaction. Distinguishing advantages and barriers as well as reducing restrictions will have an essential role in accelerating the use of this technology.

Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.

Purpose: Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods: A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results: Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion: The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.

Telecardiology interventions for patients with cardiovascular Disease: A systematic review on characteristics and effects.

INTRODUCTION: The prevalence and mortality of cardiovascular diseases are high worldwide. Telecardiology can be used to diagnose and treat these diseases. This paper aimed to review the effectiveness (positive and negative) of implemented telecardiology services in terms of clinical, economic, and patient-reported aspects. METHODS: A comprehensive search was conducted in Medline (through PubMed), Scopus, ISI web of science, and IEEE Xplore databases from inception to April 7, 2021. the studies that examined the effectiveness of telecardiology interventions were included. RESULTS: Fifty studies were included in this systematic review. Most investigations (22%) were conducted in the US. In 22% of studies, telecardiology intervention was used for patients with heart failure. Telecardiology has been used in most studies for tele-monitoring (n = 21, 42%) and tele-consultation (n = 17, 34%) and in 29 studies (58%), was applied for ECG transmission. The highest rate of effects reported by studies was clinical. Thirty-five studies (70%) reported the clinical effects; twenty-one studies reported the positive effects for the economic category, and fifteen studies reported the positive effect for patient-reported class. The most positive clinical effects of telecardiology were early diagnosis, early treatment, and mortality reduction. The most positive effect of the economic class was the reduction of health care costs. The most effects of the patient-reported category were improving the patient's quality of life and patient satisfaction. CONCLUSION: Telecardiology can help early diagnosis and treatment of cardiovascular diseases. It also has great potential in reducing health care costs and increasing quality of life and patient satisfaction.

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods.

BACKGROUND: Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2∗2 max-pooling layers-with strides 2-was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. RESULTS: The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). CONCLUSION: This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.

Applying Game-Based Approaches for Physical Rehabilitation of Poststroke Patients: A Systematic Review.

OBJECTIVE: A large number of patients need critical physical rehabilitation after the stroke. This study aimed to review and report the result of published studies, in which newly emerged games were employed for physical rehabilitating in poststroke patients. MATERIALS AND METHODS: This systematic review study was performed based on the PRISMA method. A comprehensive search of PubMed, Scopus, IEEE Xplore Digital Library, and ISI Web of Science was conducted from January 1, 2014, to November 9, 2020, to identify related articles. Studies have been entered in this review based on inclusion and exclusion criteria, in which new games have been used for physical rehabilitation. RESULTS: Of the 1326 retrieved studies, 60 of them met our inclusion criteria. Virtual reality-oriented games were the most popular type of physical rehabilitation approach for poststroke patients. "The Nintendo Wii Fit" game was used more than other games. The reviewed games were mostly operated to balance training and limb mobilization. Based on the evaluation results of the utilized games, only in three studies, applied games were not effective. In other studies, games had effective outcomes for target body members. CONCLUSIONS: The results indicate that modern games are efficient in poststroke patients' physical rehabilitation and can be used alongside conventional methods.

Using data mining techniques to fight and control epidemics: A scoping review.

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review.

BACKGROUND AND OBJECTIVE: Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. METHODS: A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. RESULTS: Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. CONCLUSIONS: In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods.