Designing a model for implementing diagnosis-related groups in Iran: An action plan approach.

Background and Aims: Implementing diagnosis-related groups (DRGs) in different countries increases the efficiency of healthcare services, improves treatment quality, and reduces treatment costs. Due to the lack of a coherent model for its implementation, the present study aimed to develop a DRGs-based implementation action plan Model for Iran. Methods: The present study was an applied, descriptive cross-sectional study conducted in three stages. In the first stage, a review of studies conducted in different countries was carried out. In the second stage, a model was designed for an action plan to implement the DRGs in Iran. In the third stage, the model was validated based on the Delphi technique. Results: The DRGs-based implementation action plan model in Iran was designed in three primary axes, including the strategic approach of the DRGs-based implementation action plan, technical dimensions, and executive institutions involved in the DRGs-based implementation action plan. Validation of the designed model showed the agreement of experts (94%) for the mentioned axes. Conclusion: The significance of tailoring a DRGs-based implementation action plan to each country's unique context is well-established. Given the intricacies of the Iranian healthcare system, we recommend an initial pilot implementation of DRGs at the hospital level, followed by a gradual national rollout.

Oncological Applications of Quantum Machine Learning.

Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.

Promotion of training course on ICD-10 Poisoning coding : necessity to adopt preventive strategies.

BACKGROUND: Poisoning is considered the most common cause of referral to emergency departments and hospitalization in the intensive care unit (ICU). Training or retraining of coders and ensuring the positive impact of these trainings in assigning accurate codes to poisoning cases is necessary to adopt practical health measures for optimal management of this disease. The present study aimed to evaluate the impact of holding a training course on poisoning coding rules based on ICD-10 in clinical coders. METHODS: This study is descriptive and analytical. With the target population included the coders of hospitals affiliated with Shahid Beheshti University of Medical Sciences (N = 45). In order to evaluate the training course on poisoning coding rules, the Conex Input Process Product (CIPP) evaluation model was used. This model was the first goal-oriented approach evaluation model. According to the CIPP model, evaluation of the training course held in four components, including Context factors (course objectives and priority of objectives), Input factors (instructor, curriculum, facilities, equipment, and training location), Process factors (teaching process, learning, management, and support), and Product factors (feedback, knowledge, and skills). A researcher-made questionnaire containing 39 questions with a 5-point Likert scale was used to collect data. The validity of the questionnaire was calculated through content validity, and its reliability was calculated using Cronbach's alpha coefficient (alpha = 90% in all components). In order to analyze the data, descriptive statistics (frequency percentage distribution) and inferential statistics (one-sample t-test) were used. RESULTS: The findings of this study were presented in four components of context, input, process, and product evaluation. The average criterion for all questions in the questionnaire was considered 3. As a result, the significance level obtained from the one sample t-test was equal to P = 0. 0001.The training course had a favorable effect in terms of context, input, process and products. CONCLUSION: The knowledge and skills of clinical coders can be enhanced by updating medical knowledge, holding training courses, workshops, seminars, and conducting clinical coder accreditation. Extensive and continuous training for clinical coders is essential due to the impact of code quality on financial forecasting, electronic health records, and conducting research.

Situation analysis model of hospital emergency department promotion in Iran: A cross-sectional study.

Background and Aim: The present study was conducted to develop a situation analysis model for Iran's hospitals' emergency departments (EDs). Methods: The current research was a descriptive cross-sectional applied study in three stages. The studies were reviewed in various library resources and valid sites in the first stage. In the second stage, the analysis model of the ED in Iran was presented. In the third stage, the model was validated based on the Delphi technique, and the final model was presented. Results: The final situation analysis model of ED in Iran was approved in four main aspects, including goals, internal factors, external factors, and organizations and institutions participating in the situation analysis, and its implementation schedule was approved by 90% of experts. Conclusion: Considering the importance of situation analysis in developing a strategic plan and improving the quality of health services in the ED of hospitals, implementing a coherent situation analysis model that includes all aspects leading to improving the ED quality and analyzing the internal and external factors is vital.

COVID-19 vaccine registry for pregnant women: policy to control complications of vaccination in pregnant women in 2021-2022.

BACKGROUND: Data management related to COVID-19 vaccination in pregnant women is vital to improve the treatment process and to establish preventive programs. Implementing a registry to manage data is an essential part of this process. This study aims to design a national model of the COVID-19 vaccination registry for pregnant women in Iran. METHODS: The present study is an applied descriptive study conducted in 2021 and 2022 in two stages. In the first stage, the coordinates of the National Registry of COVID-19 vaccination of pregnant women from related references and articles, as well as the comparative study of the National Registry of COVID-19 vaccination of pregnant women in the United States, Canada, and the United Kingdom was done. In the second stage, the preliminary model was designed. The model was validated using the Delphi technique and questionnaire tools and analyzing the data. RESULTS: The presented national COVID-19 vaccination registry model of pregnant women's main components consist of objectives, data sources, structure, minimum data set, standards, and registry processes, all of which received 100% expert consensus. CONCLUSION: The vaccination registry of pregnant women has a major role in managing COVID-19 vaccination data of pregnant women and can be one of the Ministry of Health and Medical Education priorities.

Machine learning applications for early detection of esophageal cancer: a systematic review.

INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. METHODS: We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review. RESULTS: The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. CONCLUSION: Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Development and usability evaluation of a mHealth application for albinism self-management.

BACKGROUND: Reduced or absence of melanin poses physical, social, and psychological challenges to individuals with albinism. Mobile health (mHealth) applications have the potential to improve the accessibility of information and services while reducing time and costs. This study aimed to develop and evaluate a mHealth application for self-management of albinism. METHODS: This applied study was conducted in two stages (development and evaluation) in 2022. Initially, the functional requirements were determined, and the conceptual model of the application was then developed using Microsoft Visio 2021. In the second phase, the application was evaluated using the Mobile Application Usability Questionnaire (MAUQ) involving patients with albinism to reflect their views on the usability of the application. RESULTS: The key capabilities of the application included: reminders, alerts, educational content, useful links, storage and exchange of images of skin lesions, specialist finder, and notifications for albinism-relevant events. Twenty-one users with albinism participated in the usability testing of the application. The users were predominantly satisfied with the application (5.53 ± 1.10; Max: 7.00). CONCLUSIONS: The findings of this study suggest that the developed mobile application could assist individuals with albinism to effectively manage their condition by considering the users' requirements and services that the application should deliver.

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.

Background: Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up. Materials and methods: This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype. Results: The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals. Conclusion: This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

Cervical cancer survival prediction by machine learning algorithms: a systematic review.

BACKGROUND: Cervical cancer is a common malignant tumor of the female reproductive system and is considered a leading cause of mortality in women worldwide. The analysis of time to event, which is crucial for any clinical research, can be well done with the method of survival prediction. This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer. METHOD: An electronic search of the PubMed, Scopus, and Web of Science databases was performed on October 1, 2022. All articles extracted from the databases were collected in an Excel file and duplicate articles were removed. The articles were screened twice based on the title and the abstract and checked again with the inclusion and exclusion criteria. The main inclusion criterion was machine learning algorithms for predicting cervical cancer survival. The information extracted from the articles included authors, publication year, dataset details, survival type, evaluation criteria, machine learning models, and the algorithm execution method. RESULTS: A total of 13 articles were included in this study, most of which were published from 2018 onwards. The most common machine learning models were random forest (6 articles, 46%), logistic regression (4 articles, 30%), support vector machines (3 articles, 23%), ensemble and hybrid learning (3 articles, 23%), and Deep Learning (3 articles, 23%). The number of sample datasets in the study varied between 85 and 14946 patients, and the models were internally validated except for two articles. The area under the curve (AUC) range for overall survival (0.40 to 0.99), disease-free survival (0.56 to 0.88), and progression-free survival (0.67 to 0.81), respectively from (lowest to highest) received. Finally, 15 variables with an effective role in predicting cervical cancer survival were identified. CONCLUSION: Combining heterogeneous multidimensional data with machine learning techniques can play a very influential role in predicting cervical cancer survival. Despite the benefits of machine learning, the problem of interpretability, explainability, and imbalanced datasets is still one of the biggest challenges. Providing machine learning algorithms for survival prediction as a standard requires further studies.

A Mobile Self-Management App (CanSelfMan) for Children With Cancer and Their Caregivers: Usability and Compatibility Study.

BACKGROUND: Despite the increasing development of different smartphone apps in the health care domain, most of these apps lack proper evaluation. In fact, with the rapid development of smartphones and wireless communication infrastructure, many health care systems around the world are using these apps to provide health services for people without sufficient scientific efforts to design, develop, and evaluate them. OBJECTIVE: The objective of this study was to evaluate the usability of CanSelfMan, a self-management app that provides access to reliable information to improve communication between health care providers and children with cancer and their parents/caregivers, facilitating remote monitoring and promoting medication adherence. METHODS: We performed debugging and compatibility tests in a simulated environment to identify possible errors. Then, at the end of the 3-week period of using the app, children with cancer and their parents/caregivers filled out the User Experience Questionnaire (UEQ) to evaluate the usability of the CanSelfMan app and their level of user satisfaction. RESULTS: During the 3 weeks of CanSelfMan use, 270 cases of symptom evaluation and 194 questions were recorded in the system by children and their parents/caregivers and answered by oncologists. After the end of the 3 weeks, 44 users completed the standard UEQ user experience questionnaire. According to the children's evaluations, attractiveness (mean 1.956, SD 0.547) and efficiency (mean 1.934, SD 0.499) achieved the best mean results compared with novelty (mean 1.711, SD 0.481). Parents/caregivers rated efficiency at a mean of 1.880 (SD 0.316) and attractiveness at a mean of 1.853 (SD 0.331). The lowest mean score was reported for novelty (mean 1.670, SD 0.225). CONCLUSIONS: In this study, we describe the evaluation process of a self-management system to support children with cancer and their families. Based on the feedback and scores obtained from the usability evaluation, it seems that the children and their parents find CanSelfMan to be an interesting and practical idea to provide reliable and updated information on cancer and help them manage the complications of this disease.

Self-care for coronavirus disease through electronic health technologies: A scoping review.

Background and Aims: Considering the rapid spread and transmission of COVID-19 and its high mortality rate, self-care practices are of special importance during this pandemic to prevent and control the spread of the virus. In this regard, electronic health systems can play a major role in improving self-care practices related to coronavirus disease. This study aimed to review the electronic health technologies used in each of the constituent elements of the self-care (self-care maintenance, self-care monitoring, and self-care management) during the COVID-19 pandemic. Methods: This scoping review was conducted based on Arksey and O'Malley's framework. In this study, the specific keywords related to "electronic health," "self-care," and "COVID-19" were searched on PubMed, Web of Science, Scopus, and Google. Results: Of the 47 articles reviewed, most articles (27 articles) were about self-care monitoring and aimed to monitor the vital signs of patients. The results showed that the use of electronic health tools mainly focuses on training in the control and prevention of coronavirus disease during this pandemic, in the field of self-care maintenance, and medication management, communication, and consultation with healthcare providers, in the field of self-care management. Moreover, the most commonly used electronic health technologies were mobile web applications, smart vital signs monitoring devices, and social networks, respectively. Conclusion: The study findings suggested that the use of electronic health technologies, such as mobile web applications and social networks, can effectively improve self-care practices for coronavirus disease. In addition, such technologies can be applied by health policymakers and disease control and prevention centers to better manage the COVID-19 pandemic.

Challenges of implementing diagnostic-related groups and healthcare promotion in Iran: A strategic applied research.

Background and Aim: Implementing the diagnostic-related groups (DRGs) promotes the efficiency of healthcare. Therefore, the present study aimed to identify the challenges facing implementing the DRGs in Iran. Methods: The present study is a strategic applied research conducted in two phases. In the first phase, the challenges facing DRGs were extracted through a literature review. Then the collected data is entered into a checklist consisting of five sections including technological, cultural, organizational, strategic, and natural challenges. In the second phase, data were collected by purposive sampling and semistructured interviews with 10 managers of the Medical Services Organization of Tehran, Iran. Data analysis was performed by conventional content analysis using MAXQDA software and descriptive using SPSS software version 19. Results: The challenges facing the implementing DGRs from the experts' perspective included technological, organizational, nature, strategic, and cultural in order of priority. The three main fundamental challenges were reported; lack of integrating the DGRs with health information system (70%), frequent changes of management (70%), reducing the quality of care following early patient discharge (60%). Conclusion: The results of the present study showed that the DRG system faced with challenges and healthcare officials should apply policies and guidelines to reform the system before changing the reimbursement system in Iran. By considering the leading countries experiences in the nationalizing the DRG system field, the problems and solutions of the system can be identified and aid in the more successful implementation of these systems.

Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review.

We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.

Usability of Emergency Department Information System Based on Users' Viewpoint; a Cross-Sectional Study.

Introduction: The emergency department is of special importance due to its emergency and vital services, the high volume of referrals, and the patients' physical condition. Thus, it requires a well-designed information system with no usability problems. This study aimed to evaluate the usability of the emergency department information system from users' perspectives. Methods: This was a cross-sectional study. The research setting was the emergency department of 3 hospitals. The research instrument was a 37-item questionnaire adapted from the USE and ISO Metrics questionnaires, consisting of five dimensions measuring the usefulness of the system, ease of use, ease of learning, user satisfaction, and suitability for the task. The content validity of the questionnaire was examined using the content validity ratio and content validity index, and its reliability was assessed using Cronbach's alpha (α = 0.88). Results: Fifty questionnaires were administered in the three hospitals, and the response rate was 80%. According to the findings, 55% of the respondents were female. The highest mean scores belonged to usefulness in emergency department information system (EDIS) A, ease of use in EDIS B, ease of learning in EDIS A, user satisfaction in EDIS C, and suitability for the task in EDIS A. According to the usability evaluation criteria, ease of learning (3.66 ± 0.74), usefulness (3.53 ± 0.87), and suitability for the task (3.47 ± 0.96) received the highest scores, and the lowest scores belonged to user satisfaction (3.29 ± 1.01) and ease of use (3.12 ± 1.00). Conclusion: In terms of usability criteria, the emergency department information system is at a relatively good level. The usability of these systems can be further enhanced by considering the users' working needs, improving software flexibility, customizing the software, using data visualization tools, observing consistency of features and standards, and increasing the quality of information and system services.

Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis.

OBJECTIVES: All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model. METHODS: Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014-2021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression. RESULTS: RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%). CONCLUSIONS: The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.

Design and Evaluation of an Electronic Information Exchange System Connecting Laboratories and Physicians' Offices.

Laboratory services are a crucial part of medical care and contribute to physicians' treatment-related decision-making. However, paper-based information exchanges between physicians' offices and laboratories waste physicians' time and prevent them from using outpatient test results in a timely and effective manner. To solve this problem, improve the safety and quality of patient care, and save patients' time and energy, the present study developed a web-based system for electronic information exchange between laboratories and offices in Microsoft Visual Studio with the ASP.net technology and the Microsoft SQL Server database. The developed web-based software met the needs of the users and stakeholders (physicians, laboratory personnel, and patients) in the laboratory service cycle. To evaluate the software, user satisfaction was assessed in terms of user interface, operational functionality, and system performance, indicating the acceptability of all the criteria from the viewpoint of the stakeholders. The developed web-based software enables electronic communication between offices and laboratories (two important healthcare bases), establishes information exchange (sending requests and receiving laboratory results) between these two bases, and also notifies the patients. The software gained the overall satisfaction of the users, and this highlights the need for electronic communications in the healthcare domain.

Identifying predictors of varices grading in patients with cirrhosis using ensemble learning.

OBJECTIVES: The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis. METHODS: In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed. RESULTS: The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR). CONCLUSIONS: Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study.

OBJECTIVES: The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis. METHODS: In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms. RESULTS: According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%. CONCLUSIONS: We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.

Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.

Introduction: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method: Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results: We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion: Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.

Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review.

Method: This study was conducted according to Arksey and O'Malley's framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020. Results: Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients. Conclusion: The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients.