Evaluation and Ranking of Persian Mobile Apps for COVID-19

Introduction: Recently developed mobile apps for controlling COVID-19 have the potential to help fight the pandemic. But assurance regarding the quality of available apps is essential to proving their validity for usage. This study was aimed at evaluating and ranking the apps in Persian developed for COVID-19 in Iran. Material and Methods: 122 apps for COVID-19 in the Persian language were founded in the Miket, CafeBazar, ParsHub, and Charkhooneh app markets. Based on inclusion criteria, 13 apps were selected. The apps were evaluated by two independent reviewers and ranked according to a validated evaluation and ranking tool specifically for the Persian apps for information content, usability, design, ethics, security, privacy, and subjective quality. Kendall’s coefficient of concordance was used to calculate the agreement between two raters based on the mean of their scores for each app (p-value<0.05). Results: Five functional and subjective quality criteria were used. Mask was the app with the highest level of the specific score (mean score: 4.10, subjective quality: 4). The Corona test-Davoudi was the app with the lowest level of the specific score (mean score: 1.85, subjective quality: 1.50), which needs more improvement. The reviewed apps mainly need improvement for data security and privacy, requiring more technical tasks. Conclusion: There is a need for improvement, particularly in terms of privacy and data security, for Persian COVID-19 apps. Develop a valid guideline that could be effective in improving app quality. In addition, the modern technologies that have already proven successful worldwide should be considered by mobile app developers

Comparing performances of intelligent classifier algorithms for predicting type of pain in patients with spinal cord injury

Background and aim: In this study, performances of classification techniques were compared in order to predict type of pain in patients with spinal cord injury. Pain is one of the main problems in people with spinal cord injury. Identifying the optimal classification technique will help improve decision support systems in clinical settings. Methods: A descriptive retrospective analysis was performed in 253 patients. We compared performances of "Bayesian Networks", "Decision Tree", neural networks: “Multi-Layer Perceptron” (MLP), and "Support Vector Machines” (SVM). Predictor variables were collected in data set in SCI patients referred to Shefa Neuroscience Research Center, Tehran, Iran from 2010 through 2016. Performances of classification techniques were compared using ”Accuracy”, ”Sensitivity or True Positive Rate” (TPR), ”Specificity or True Negative Rate” (SPC), ”Positive Predictive Value” (PPV), ”Negative Predictive Value” (NPV). Results: MLP with Boosting technique was found to have the best accuracy (91%), best sensitivity (89%), best specificity (95%) best PPV (91%), and best NPV (96%) to predict spinal cord injury in this data set, given its good classificatory performance. Conclusion: Computer-aided decision support systems (CAD) are dependent on a wide range of classification methods such as statistical methods, Bayesian methods, deductive classifiers based on the state or case, decision- making trees and neural networks: Multi-Layer Perceptron. Neural network classifiers especially, are very popular choices for medical decision-making, with proven effectiveness in the clinical field

DESIGN OF AN INTELLIGENT SYSTEM TO DETECT TYPE OF PAIN USING ARTIFICIAL NEURAL NETWORK FOR PATIENTS WITH SPINAL CORD INJURY IN SHEFA NEUROSCIENCE RESEARCH CENTER

Using artificial intelligence in computerized clinical systems helps physicians diagnose disease or choose treatment. Intelligent methods are constantly changed to be more effective and accurate for quick medical diagnosis. Neural networks are a powerful tool to help physicians. The tools can process a high number of data and minimize errors in ignoring patients' information. Intelligent system design based on artificial neural network was performed in 3 phases. Phase1: Designing the data recording and collection system. Phase2: Working with data and samples. Phase3: Artificial neural network design and analysis. Within 7 months, the data pertaining to 253 patients were collected and recorded in Shefa Neuroscience Center. Models of artificial neural network generated and for all models, the precision, sensitivity, attributes, positive reported value and negative reported value were calculated for comparison. 30 models of neural networks were generated. Performing various categorization methods on differing data shows that these methods do not have similar performance. At primary stage, model accuracy was 54%. We implemented the “Bagging” and “Boosting” performance improvement techniques in order to improve the values needed by the models. Accuracy model in secondary stage showed a 91% improvement in comparison with physician diagnosis. Neural network classifiers are very popular choices for medical decision-making, with proven effectiveness in clinical field. A number of studies have indicated that these networks may have significant prediction performance as compared to other methods. In the field of medicine, there are several practical challenges and restrictions regarding data collection. Keywords: Pain, Pain diagnosis, classification, Spinal Cord Injury, Artificial Intelligence, Artificial Neural Networ

Preconceived stakeholders' attitude toward telepathology: Implications for successful implementation

Introduction: Telepathology is a subdiscipline of telemedicine. It has opened new horizons to pathology, especially to the field of organizing consultations. This study aims to determine the capabilities and equipment required for the implementation of telepathology from the viewpoints of managers, IT professionals, and pathologists of the hospitals of West Azerbaijan, Iran. Methods: This is a descriptive-analytical study conducted as a cross-sectional study in 2015. All public and private hospitals of West Azerbaijan were selected as the study sites. The population of the study was the managers, directors, pathologists, and IT professionals of the hospitals. The study population was considered as the study sample. Data were collected using questionnaires. The validity and reliability of the questionnaires were assessed, and data were analyzed using SPSS (Statistical Product and Services Solutions, version 16.0, SPSS Inc, Chicago, IL, USA). Results: The mean awareness of the study population of telepathology in the studied hospitals was 2.43 with a standard deviation of 0.89. According to analysis results (F = 7.211 and P = 0.001), in the studied hospitals, the mean awareness of pathologists, managers, directors, and IT professionals' of telepathology is significant. In addition, the mean awareness of pathologists is higher than that of managers, directors, and IT professionals, and this relation is significant (P = 0.001). According to IT professionals, among the influential dimensions of the implementation of telepathology in the studied hospitals, the effect of all dimensions, except hardware capabilities, was above moderate level. Conclusion: According to our findings, stakeholders believe that the implementation of telepathology promotes the quality of health-care services and caring patients on the one hand and decreases health-care costs on the other hand. Therefore, it crucial and important to consider users' viewpoints into the process of implementing such systems as they play a vital role in the success or failure, and the accurate estimation of required sources, of the systems