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آشکارسازی مردمک به منظور تشخیص خودکار بیماری‌های چشم با استفاده از نگاشت رنگی بهینه Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping 1 1 مقدمه: دسته‌ مهمی از بیماری‌های مربوط به چشم شامل اختلالات مردمک و عنبیه چشم است. بخش‌بندی دقیق مردمک اولین و مهم‌ترین گام در تشخیص خودکار بیماری‌های مربوط به مردمک و عنبیه چشم می‌باشد. اکثر روش‌های موجود دقت کافی نداشته و نسبت به اثرات نویز و انعکاس مؤلفه‌های نوری، حساس می‌باشند. ضمن این که تصاویر استفاده شده در آن‌ها نیز معمولاً محدودیت‌هایی از جمله زاویه دید را دارند. روش: در الگوریتم پیشنهادی ابتدا روشی پایدار جهت حذف مؤلفه‌های انعکاس در مردمک و پیش پردازش‌های لازم به منظور تشخیص محل دقیق مردمک پیشنهاد شده است. سپس یک الگوریتم نگاشت رنگی بهینه به کمک الگوریتم لونبرگ-مارکوارت برای تعیین دقیق مرز مردمک محاسبه شده است. این روش هیچ‌گونه اعمال محدودیتی بر روی تصویر چشم نداشته و شکل و زاویه دید مردمک در تصویر به هر شکل و در هر جهتی می‌تواند باشد. نتایج: روش پیشنهادی هیچ مدل خاصی (دایره یا بیضی) را به عنوان مرز نهایی مردمک در نظر نگرفته و نسبت به نوفه و انعکاس نور  مقاوم است. این روش توانسته در تشخیص دقیق مرز مردمک با نرخ تشخیص 98/8 و 98 درصد به ترتیب برای پایگاه داده UBIRIS و پایگاه داده گردآوری شده، عملکرد بهتری در مقایسه با الگوریتم‌های مهم ارائه شده در سال‌های اخیر داشته باشد. نتیجه‌گیری: از روش ارائه شده در این پژوهش می‌توان برای افزایش دقت در بخش‌بندی مرز داخلی و خارجی عنبیه به منظور تشخیص بیماری‌های مربوط به مردمک و عنبیه چشم، همچنین جهت تشخیص هویت از طریق عنبیه چشم استفاده کرد. 2 Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle. Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction. Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors. Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue. 1 17 2023/02/5 1401/11/16 2023/03/27 1402/1/7 رسول خیرالهی Rasul Kheyrolahi‌ کارشناسی ارشد مهندسی برق، گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه صنعتی سهند تبریز، تبریز، ایران ایران kheirolahi.rs@gmail.com فاطمه حورعلی Fatemeh Hoorali دکتری مهندسی برق، استادیار، گروه مهندسی برق، دانشکده مهندسی برق و کامپیوتر، مجتمع آموزش عالی فنی و مهندسی اسفراین، خراسان شمالی، ایران ایران f.hourali@esfarayen.ac.ir محمد حسین صداقی Mohammad Hossein Sedaaghi دکتری مهندسی برق، استاد، گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه صنعتی سهند تبریز، تبریز، ایران ایران sdaaghi@sut.ac.ir حسین ابراهیم نژاد Hossein Ebrahimnezhad دکتری مهندسی برق، استاد، گروه مهندسی برق، دانشکده مهندسی برق، دانشگاه صنعتی سهند تبریز، تبریز، ایران ایران ebrahimnezhad@sut.ac.ir Disease Diagnosis Segmentation Iris Pupil Color Mapping تشخیص بیماری بخش‌بندی عنبیه مردمک نگاشت رنگی 1. 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Deep learning-based pupil center detection for fast and accurate eye tracking system. In Computer Vision–ECCV 2020: 16th European Conference; 2020 Aug; Glasgow, UK: 2020. p. 36-52.##12. Fathee HN, Sahmoud S, Abdul-Jabbar JM. A Robust Iris Segmentation Algorithm Based on Pupil Region for Visible Wavelength Environments. 3rd International Seminar on Research of Information Technology and Intelligent Systems (IEEE); 2020 Dec 10-11; Yogyakarta, Indonesia: 2020. p. 655-60. doi: 10.1109/ISRITI51436.2020.9315343##13. Sahmoud S, Fathee HN. Fast iris segmentation algorithm for visible wavelength images based on multi-color space. In 20th International Conference on Advanced Concepts for Intelligent Vision Systems; 2020 Feb; Auckland, New Zealand: 2020. p. 239-50.##14. Kim J, Stengel M, Majercik A, De Mello S, Dunn D, Laine S, et al. Nvgaze: An anatomically-informed dataset for low-latency, near-eye gaze estimation. 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ارزیابی بیوانفورماتیک و سیگنالی مجموع mRNA هدف و عملکرد SNP مرتبط با آن در افراد مبتلا به سرطان معده Bioinformatics Evaluation and Signaling Network Analysis of Total Target mRNA and Related SNP Function in People with Gastric Cancer 1 1 مقدمه: سرطان معده در جهان به عنوان چهارمین سرطان شایع و دومین عامل مرگ بر اثر سرطان شناخته می‌شود. که علاوه بر هزینه‌های قابل توجه، مشکلات عدیده‌ای را بر خانواده‌ها تحمیل نموده که خود توجیه مناسبی جهت شناسایی بیومارکرهای مرتبط می‌باشد. روش: برای یافتن اطلاعات بیوانفورماتیکی از وبسایت miRBASE  استفاده شد. همچنین پایگاه های داده  mirWALK و miRNASN برای یافتن اطلاعات مربوط به‌miRNA ها جستجو شدند . برای یافتن مسیر سرطانی از پایگاه های داده‌ DAVID database و Kegg استفاده شد.  نتایج: مطالعات انجام شده در اولین مسیر، میکرو RNA، has-miR-10a-3p، با اتصال به rs1049216 در ژن CASP3،  با اعمال اثر مهاری بر این ژن، از ایجاد سرطان جلوگیری می‌نماید. در دومین مسیر، میکرو RNA، has-miR-296-3p، با اتصال به rs1564483 در ژن BCL2، با اعمال اثر مهاری بر این ژن، باعث ایجاد سرطان می‌شود. در سومین مسیر، میکرو RNA، has-miR-194-3p، با اتصال به rs7177 در ژن CCND1، با اعمال اثر مهاری بر این ژن، از ایجاد سرطان جلوگیری می‌نماید. نتیجه‌گیری: در این پژوهش مسیرهای سرطانی ای پیش‌بینی شد که دخیل در سرطان معده هستند،  با یافتن سه SNP بر روی مناطق خاصی از این سه ژن، دریافتیم که این SNPها می‌توانند عامل ایجاد سرطان معده باشند و می‌توانند به عنوان بیومارکرهای زیستی معرفی و به کار روند. 2 Introduction: Stomach cancer is the fourth most common cancer and the second leading cause of cancer death in the world. In addition to significant costs, it imposes many problems on families that is a proper justification for identifying related biomarkers. Method: The miRBASE website was used to find bioinformatics information. In addition, mirWALK and miRNASN databases were searched to find miRNAs information. To find cancer pathways, the DAVID and KEGG databases were adopted. Results: The studies conducted on the first pathway reported that microRNA and has-miR-10a-3p, through binding to rs1049216 in the CASP3 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. In the second pathway, microRNA and has-miR-296-3p, by binding to rs1564483 in the BCL2 gene, by exerting an inhibitory effect on this gene, causes cancer. In the third pathway, microRNA and has-miR-194-3p, through binding to rs7177 in the CCND1 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. Conclusion: In this research, cancer pathways involved in cancer were predicted. By finding three SNPs on specific regions of the three above-said genes, we found that these SNPs can be the cause of stomach cancer and can be introduced as biological biomarkers. 18 27 2023/02/52022/12/8 1401/9/17 2023/03/272023/03/7 1401/12/16 مهناز محمدی Mahnaz Mohammadi دکتری زیست شناسی، استادیار، گروه زیست شناسی، واحد اسلامشهر دانشگاه آزاد اسلامی، اسلامشهر، ایران ایران mh_mohamadi@yahoo.com پریسا امین الرعایائی Parisa Aminoroayaei کارشناس ارشد بیوتکنولوژی، گروه بیوتکنولوژی، دانشکده علوم پزشکی آزاد تهران، ایران ایران parisa.amin273@gmail.com آرزو نظری Arezo Nazari کارشناس ارشد بیوتکنولوژی، گروه بیوتکنولوژی، دانشکده علوم پزشکی آزاد تهران، ایران ایران arna1369.an@gmail.com Bioinformatics micro RNA Gastric Cancer SNP Targeted Medicine Helicobacter pylori بیوانفورماتیک میکرو RNA سرطان معده SNP درمان هدفمند هلیکوباکترپیلوری 1. Whiting JL, Sigurdsson A, Rowlands DC, Hallissey MT, Fielding JW. 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ارائه و ارزیابی مدل معماری بیمه سلامت گردشگری هوشمند مبتنی بر IoT و ابر Introducing and Evaluating an Architectural Model for Smart Tourism Health Insurance based on IoT and Cloud 1 1 مقدمه: گردشگری به عنوان یکی از برنامه‌های کسب درآمد منجر به رشد اقتصادی می‌‌شود، اما با توجه به عدم ارائه خدمات پزشکی و بهداشتی توسط سازمان‌های بیمه‌ای، صنعت گردشگری در ایران با کاهش استقبال مواجه شده است. این پژوهش سعی دارد با توجه به این موضوع، معماری بیمه سلامت گردشگری هوشمند در بستر اینترنت اشیاء (IoT) و ابر را برای خدمت به گردشگران ارائه دهد. روش: این پژوهش یک مطالعه کاربردی است که جهت رسیدن به معماری بیمه سلامت گردشگری هوشمند طراحی شد. ابتدا نیازمندی‌‌های آن از طریق مطالعات کتابخانه‌ای و مصاحبه باز با چهار کارشناس بیمه ایران انجام شد. سپس با بررسی انواع مختلف مدل‌های معماری بیمه سلامت گردشگری، معماری پیشنهادی مبتنی بر سرویس‌گرا-لایه‌ای در بستر IoT و ابر ارائه گردید. در نهایت معماری پیشنهادی توسط روش ATAM مورد ارزیابی قرار گرفت. نتایج: معماری پیشنهادی دارای دو مؤلفه درخواست‌دهنده و فراهم‌کننده خدمت با لایه‌های کاربر، شبکه، فضای ابری و پایگاه داده است که به صورت خدمت‌محور به یکدیگر متصل هستند؛ همچنین ارزیابی معماری پیشنهادی توسط روش ATAM نشان داد که معماری ارائه شده قابلیت‌پاسخگویی به نیازهای سلامت گردشگران را دارد. نتیجه‌گیری: این معماری در بستر  IoT و ابر ارائه شده است که زمینه هوشمندی را فراهم می‌کند و با ثبت داده‌های افراد در پروفایل ریسک شخصی، امکان ارائه تعرفه‌های خاص و کاهش هزینه‌های گردشگری را ممکن می‌سازد؛ بنابراین گردشگران با اطمینان خاطر بیشتری نسبت به کنترل وضعیت سلامتی خود، قادر به سفر خواهند بود. 2 Introduction: Tourism as one of the programs to earn money leads to economic growth; however, due to the non-providing of medical and health services by insurance organizations, the Iranian tourism industry has faced a recession. Therefore, this study aimed to provide a smart tourism health insurance architecture in the context of the Internet of Things (IoT) and cloud to serve tourists. Method: This practical study was conducted to achieve a smart tourism health insurance architecture. First, its requirements were determined by reviewing the related literature and open interview with four experts from Iran Insurance Corporation. Then, by examining different types of tourism health insurance architectural models, a proposed service-oriented layered architecture was presented on the platform of IoT and cloud. Finally, the proposed architecture was evaluated by the ATAM method. Results: The proposed architecture has two components (i.e., the requester and the service provider) with user, network, cloud, and database layers that are connected in a service-oriented manner. Also, the evaluation of the proposed architecture by the ATAM method showed that it can respond to the health needs of tourists. Conclusion: This architecture is presented on the platform of IoT and cloud that provides an intelligent background, and by recording people's data in personal risk profiles, it facilitates providing special tariffs and reducing tourism costs. Therefore, tourists will be able to travel with more confidence in controlling their health status. 28 40 2023/02/52022/12/82023/01/3 1401/10/13 2023/03/272023/03/72023/03/7 1401/12/16 فاطمه هاشم بیگی Fatemeh Hashembeigi دانشجوی کارشناسی ارشد انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران F.Hashembeigi@modares.ac.ir فاطمه مشیری Fatemeh Moshiri دانشجوی کارشناسی ارشد انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران moshirifateme68@gmail.com عباس آسوشه Abbas Asosheh دکترای مهندسی کامپیوتر و مخابرات (انتقال صوت)، استادیار گروه انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران asosheh@modares.ac.ir Health Insurance Smart Tourism Service-oriented Architecture Architectural Evaluation IoT Cloud بیمه سلامت گردشگری هوشمند معماری سرویس‌گرا ارزیابی اینترنت اشیاء ابر 1. Farzin MR, Shekari F, Azizi F. Tourism Destination Competitiveness: An Importance-Performance Analysis of Yazd and Shiraz. 2018;13(44):219-47. [In Persian] https://doi.org/10.22054/tms.2018.9653##2. Almobaideen W, Krayshan R, Allan M, Saadeh M. Internet of Things: Geographical Routing based on healthcare centers vicinity for mobile smart tourism destination. Technological Forecasting and Social Change 2017;123:342-50. https://doi.org/10.1016/j.techfore.2017.04.016##3. Nahid S. Health Complementary Insurance in Iran. Social Welfare Quarterly 2013;38(48): 247-70. [In Persian]##4. Kavosi M, Tabibi S J, Mahmoudi Majdabadi Farahani M, Hajinabi K. Factors Affecting Organizations Affiliated to Iran's Supportive Health Insurance System. Iran J Health Insur 2019; 1(4):165-72. [In Persian]##5. Manral J. IoT enabled Insurance Ecosystem-Possibilities Challenges and Risks. arXiv preprint arXiv:1510.03146. ##6. Ashton K. That ‘internet of things’ thing. RFID Journal 2009;22(7):97-114.##7. Setareh S, Rezaee A, Farahmandian V, Hajinazari P, Asosheh A. A cloud-based model for hospital information systems integration. 7'th International Symposium on Telecommunications; 2014 Sep 9-11; Tehran: IEEE; 2014. p. 695-700. doi: 10.1109/ISTEL.2014.7000792##8. Mell P, Grance T. The NIST definition of cloud computing. U.S. Department of Commerce; 2011.##9. Doukas C, Maglogiannis I. Bringing IoT and cloud computing towards pervasive healthcare. Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing; 2012 Jul -6; Palermo, Italy: IEEE; 2012. p. 922-6. doi: 10.1109/IMIS.2012.26##10. Smirnov AV, Ponomarev AV, Levashova TV, Teslya NN. Human–Machine Cloud Decision Support in Tourism. Scientific and Technical Information Processing 2018;45:352-9.##11. Nitti M, Pilloni V, Giusto D, Popescu V. IoT Architecture for a sustainable tourism application in a smart city environment. Mobile Information Systems 2017;2017. https://doi.org/10.1155/2017/9201640##12. Guo Y, Liu H, Chai Y. The embedding convergence of smart cities and tourism internet of things in China: An advance perspective. Advances in Hospitality and Tourism Research 2014;2(1):54-69.## ##13. Patel KK, Patel SM, Scholar P. Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges. International Journal of Engineering Science and Computing 2016;6: 6122-33.##14. Molano JI, Lovelle JM, Montenegro CE, Granados JJ, Crespo RG. Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. Journal of Ambient Intelligence and Humanized Computing 2018;9:709-23.##15. Cloud Standards Customer Council. Cloud Customer Architecture for IoT; 2016.##16. Erl T. SOA principles of service design (the Prentice Hall service-oriented computing series from Thomas Erl). Prentice Hall PTR; 2007.##17. Moshiri F, Asosheh A. Classification of ARchitectural Styles based on the Dimensions of Integration of Hospital Information Systems. Journal of Health and Biomedical Informatics 2022; 8(4): 347-58. [In Persian]##18. Moshiri F, Asosheh A. Persenting an Integration Architecture of Hospital Inormation System based on Interoperability Model: Clinical I nformation System. Journal of Health and Biomedical Informatics 2022; 9(2): 92-103. [In Persian]##19. Chatra Raveesh S. Using the Architectural Tradeoff Analysis Method to Evaluate the Software Architecture of a Semantic Search Engine: A Case Study [dissertation]. Ohio: The Ohio State University; 2013.##20. Shreelekhya G, Yazhini US, Manikandan N. Methods for evaluating software architecture-A survey. International Journal of Pharmacy and Technology 2016; 8(4):25720-33. ##21. Gambo I, Soriyan A, Achimugu P. Software Performance Quality Evaluation of MINPHIS Architecture using ATAM. International Journal of Computer Applications 2012;46(23): 30-6.##22. Roy B, Graham TN. Methods for evaluating software architecture: A survey. School of Computing TR 2008;545:82.##23. Zeinali N, Asosheh A, Setareh S, editors. The conceptual model to solve the problem of interoperability in health information systems. 8th International Symposium on Telecommunications (IST); 2016 Sep 27-28; Tehran: IEEE; 2016. doi: 10.1109/ISTEL.2016.7881909##24. Farjami Y, Asosheh A, Afshinfar A. An ERP Framework Based on Service Oriented Architecture and Cloud Computing Environment Case: IRISL Container Department. International Journal of Information and Communication Technology Research 2018; 10 (3) :60-8.##25. Putrama IM, Dermawan KT, Dantes GR, Aryanto KYE, editors. Architectural evaluation of data center system using architecture tradeoff analysis method (ATAM): A case study. 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA); 2017: IEEE.##26. Szwed P, Wojnicki I, Ernst S, Głowacz A, editors. Application of new ATAM tools to evaluation of the dynamic map architecture. 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مدل شبکه عصبی پیچشی برای شناسایی بیماری COVID-19 و ذات الریه A Convolutional Neural Network Model for Detection of COVID-19 Disease and Pneumonia 1 1 مقدمه: COVID-19 تأثیرات مخربی بر سلامت عمومی در سراسر جهان ایجاد کرده است. از آنجایی که تشخیص زودهنگام و درمان به‌موقع بر کاهش مرگ‌و میر بر اثر ابتلاء به COVID-19 تأثیر‌گذار است و روش‌های تشخیصی موجود ازجمله آزمایش RT-PCR مستعد خطا است، راه حل جایگزین استفاده از تکنیک‌های هوش مصنوعی و پردازش تصویر است. هدف کلی معرفی مدل هوشمند مبتنی بر یادگیری عمیق و شبکه عصبی پیچشی برای شناسایی موارد مبتلا به COVID-19 و ذات‌الریه به منظور اقدامات درمانی بعدی به کمک تصاویر پزشکی ریه است. روش: مدل پیشنهادی شامل دو مجموعه داده‌ رادیوگرافی و CT-scan است. تصاویر مجموعه داده مورد پیش‌پردازش قرار می‌گیرند و فرآیند تقویت داده روی تصاویر اعمال می‌شود. در مرحله بعد از سه معماری EfficientNetB4, InceptionV3 و InceptionResNetV2 با استفاده از روش یادگیری انتقالی استفاده می‌شود. نتایج: بهترین نتیجه به دست آمده برای تصاویر CT-scan متعلق به معماری InceptionResNetV2 با دقت 99/366% و برای تصاویر رادیولوژی مربوط به معماری‌ InceptionV3 با دقت 96/943% می‌باشد. علاوه بر آن نتایج حاکی از آن است که تصاویر CT-scan از ویژگی‌های بیشتری نسبت به تصاویر رادیوگرافی برخوردار بوده و تشخیص بیماری روی این نوع داده‌ها با دقت بیشتری انجام می‌پذیرد. نتیجه‌گیری: مدل پیشنهادی مبنتی بر شبکه عصبی پیچشی دقت بالاتری نسبت به سایر مدل‌های مشابه دارد. همچنین با اعلام نتایج آنی می‌تواند در ارزیابی اولیه مراجعان به مراکز درمانی به خصوص در زمان اوج همه‌گیری‌ها که مراکز درمانی با چالش‌هایی مانند کمبود نیرو متخصص و کادر درمان مواجه می‌شوند کمک نماید. 2 Introduction: COVID-19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID-19 and existing diagnostic methods such as RT-PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID-19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images. Method: The proposed model includes two datasets of radiography and CT-scan. These datasets are pre -processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3, and InceptionResNetV2 are used using transfer learning method. Results: The best result obtained for CT-scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT-scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data. Conclusion: The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges, such as lacking specialists and medical staffs. 41 56 2023/02/52022/12/82023/01/32022/12/14 1401/9/23 2023/03/272023/03/72023/03/72023/05/24 1402/3/3 سید محمد موسوی Seyed Mohammad Mousavi کارشناس ارشد علوم کامپیوتر، بخش علوم کامپیوتر، دانشکده ریاضی و کامپیوتر، دانشگاه شهید باهنر کرمان، کرمان، ایران ایران m.mousavi@math.uk.ac.ir سوده حسینی Soodeh Hosseini دکتری مهندسی کامپیوتر، دانشیار، بخش علوم کامپیوتر، دانشکده ریاضی و کامپیوتر، دانشگاه شهید باهنر کرمان، کرمان، ایران ایران so_hosseini@uk.ac.ir Image Processing Artificial Intelligence COVID-19 Convolutional Neural Network پردازش تصویر هوش مصنوعی کووید19 شبکه عصبی پیچشی 1. Sohrabi C, Alsafi Z, O'neill N, Khan M, Kerwan A, Al-Jabir A, et al. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery 2020;76:71-6. https://doi.org/10.1016/j.ijsu.2020.02.034##2. Ahmed I, Jeon G, Chehri A. An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection. Computing 2022; 105(1):1-8. doi:10.1007/s00607-021-00992-0##3. Deb SD, Jha RK, Jha K, Tripathi PS. A multi model ensemble based deep convolution neural network structure for detection of COVID19. Biomed Signal Process Control 2022; 71: 103126. doi: 10.1016/j.bspc.2021.103126##4. Islam MR, Nahiduzzaman M. Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach. Expert Systems with Applications 2022;195:116554. https://doi.org/10.1016/j.eswa.2022.116554##5. Kavya NS, Veeranjaneyulu N, Priya DD. Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks. Materials Today: Proceedings 2022;64:737-43. https://doi.org/10.1016/j.matpr.2022.05.199##6. Thakur S, Kumar A. X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN). Biomedical Signal Processing and Control 2021;69:102920. https://doi.org/10.1016/j.bspc.2021.102920##7. Elpeltagy M, Sallam H. Automatic prediction of COVID− 19 from chest images using modified ResNet50. Multimed Tools Appl 2021; 80(17): 26451–63. doi: 10.1007/s11042-021-10783-6##8. Owais M, Yoon HS, Mahmood T, Haider A, Sultan H, Park KR. Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database. Applied Soft Computing 2021;108:107490. https://doi.org/10.1016/j.asoc.2021.107490##9. 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شناسایی و تحلیل کاربردهای اینترنت اشیاء در چابکی زنجیره تأمین صنعت دارو در پساکرونا Identifying and Analyzing the Application of the Internet of Things in Supply Pharmaceutical Chain Agility in post COVID-19 1 1 مقدمه: اینترنت اشیاء به عنوان یک فناوری جدید از زمان ظهور فناوری بی‌سیم محبوبیت بیشتری پیدا کرده و توجه فعالان حوزه مدیریت زنجیره تأمین را به خود جلب کرده است. هدف این پژوهش شناسایی و تحلیل کاربردهای اینترنت اشیاء در چابکی زنجیره تأمین صنعت دارو در پساکرونا می‌باشد. روش: ابتدا با مرور مبانی نظری و مطالعات پیشین از طریق روش تحلیل محتوای کیفی، کاربردهای اینترنت اشیاء در چابکی زنجیره تأمین صنعت دارو شناسایی و تفسیر شدند؛ سپس برای تأیید و اعتبارسنجی عوامل شناسایی‌شده، این عوامل در اختیار 20 نفر از خبرگان حوزه مدیریت و فناوری اطلاعات قرار گرفت. نتایج: نتایج پژوهش نشان می‌دهد که کاربردهای اینترنت اشیاء در چابکی زنجیره تأمین صنعت دارو دارای 9 مؤلفه و 50 شاخص می‌باشد. در درجه اول مهم‌ترین عامل شناسایی‌شده، ارتباط سریع‌تر با سایر ذینفعان (تأمین‌کنندگان، تولیدکنندگان، توزیع‌کنندگان و مشتریان) با استفاده از اینترنت اشیاء در زنجیره تأمین می‌باشد. در درجه دوم ایده‌ اینترنت اشیاء برای تولید دارو واضح است؛ تولیدکنندگان در زمینه‌های تکنولوژیکی سرمایه‌گذاری‌های کلانی می‌کنند که به ‌نوبه‌ خود منجر به کاهش هزینه‌های عملیاتی در دراز مدت است چرا که دستگاه‌های شبکه و سنسورها اشتباه نمی‌کنند، به استراحت و آموزش نیاز ندارند و مرخصی نمی‌گیرند؛ بنابراین چابکی فرآیند تولید دارو را افزایش داده و یک جایگزین قابل‌اعتماد و مقرون‌به‌صرفه برای بهره‌وری در تولید محسوب می‌شوند. نتیجه‌گیری: کاربردهای گسترده مدل پیشنهادی حاکی از لزوم توجه به استفاده از اینترنت اشیاء در زنجیره تأمین صنایع دارو در جهت بهبود عملکرد کلی زنجیره تأمین و تمرکز بر چابکی زنجیره تأمین است.   2 Introduction: The internet of Things has become more popular as a new technology since the advent of wireless technology, and attracted attention from supply chain management promoters. This study aimed to identify and analyze IoT applications in the pharmaceutical supply chain agility in the post-COVID-19 era Method: We first review previous studies in applications of IoT in pharmaceutical supply chain agility and identified the findings through qualitative content analysis. Then, the identified factors were given to 20 management and information technology experts to confirm and validate. Results: The results showed that IoT applications on the pharmaceutical supply chain agility in 9 components and 50 indicators. First of all, the most important factor is faster communication with other stakeholders (suppliers, manufacturers, distributors, and customers) using the IoT in the supply chain. Secondly, the use of IoT for drug production; manufacturers invest in technologies that lead to lower operating costs in the long term, because network devices and sensors do not make mistakes, do not need rest and training, and do not take vacations. Therefore, they increase the agility of the production process and are a reliable and cost-effective alternative for productivity in production. Conclusion: The wide applications of the proposed model indicate the need to consider the use of the IoT in the pharmaceutical industry supply chain in order to improve overall supply chain performance and focus on supply chain agility.   57 69 2023/02/52022/12/82023/01/32022/12/142023/01/8 1401/10/18 2023/03/272023/03/72023/03/72023/05/242023/05/20 1402/2/30 میلاد بخشم Milad Bakhsham دانشجوی دکترای کارآفرینی، گروه مدیریت و کارآفرینی، دانشکده علوم اجتماعی، دانشگاه رازی، کرمانشاه، ایران ایران Da.miladbakhsham@gmail.com مهدی حسین پور Mahdi Hosseinpour استادیار کارآفرینی، گروه مدیریت و کارآفرینی، دانشکده علوم اجتماعی، دانشگاه رازی، کرمانشاه، ایران ایران m.hosseinpour@razi.ac.ir معصومه آینه Masoumeh Ayeneh کارشناس ارشد مدیریت بازرگانی، گرایش بازاریابی، دانشگاه پیام نور، تهران، ایران ایران masomehayneh@gmail.com حسین کریمی Hossein Karimi کارشناس ارشد مدیریت فناوری اطلاعات، گروه مدیریت و کارآفرینی، دانشکده علوم اجتماعی، دانشگاه رازی، کرمانشاه، ایران ایران Hossein.karimi_iran2020@yahoo.com پریسا پرندآور Parisa Parandavar دانشجوی کارشناسی ارشد کارآفرینی سازمانی، دانشکده علوم اجتماعی، دانشگاه رازی، کرمانشاه، ایران ایران parisaparand24@gmail.com Internet of Things Supply Chain After the COVID-19 Medicine Industry Agility اینترنت اشیاء زنجیره تأمین پساکرونا صنعت دارو چابکی 1. 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ارزیابی برنامه‌های کاربردی تلفن هوشمند در حوزه تجویز آنتی‌بیوتیک Evaluation of the Smartphone Applications in Antibiotic Prescription 1 1 مقدمه: یکی از بزرگ‌ترین چالش‌های پزشکان، انتخاب آنتی‌بیوتیک مناسب برای بیماری‎های مختلف است که در این زمینه برنامه‌های کاربردی می‌توانند کمک‌کننده باشند. هدف از انجام این مطالعه، مقایسه کیفیت برنامه‌های کاربردی تلفن هوشمند جهت کمک به انتخاب آنتی‎بیوتیک‎ها است. روش: در این مطالعه مروری، برنامه‌های کاربردی رایگان و انگلیسی زبان تصمیم‌یار جهت انتخاب آنتی‌‌بیوتیک در  Google Play Store و Apple Store با استفاده از کلیدواژه‎های مربوطه برای پلتفرم‌های Android و iOS جستجو و استخراج شدند. برنامه‌‌های وارد شده با استفاده از امتیازدهی رتبه‌ای اپلیکیشن‌های موبایلی (MARS) توسط دو ارزیاب (متخصص عفونی و داروساز) نمره‌دهی شدند. از ضریب مطابقت کندال برای محاسبه میزان توافق دو ارزیاب استفاده شد. جهت آزمون همبستگی نمرات اخذ شده توسط ابزار MARS با متغیرهای کیفی زمینه‌ای، از آزمون من‌ویتنی و متغیرهای کمی از آزمون همبستگی اسپیرمن استفاده شد. نتایج: در جستجوی اولیه ­ای که تا مهرماه 1401 انجام ­شد، 13 برنامه­ انتخاب ­شد. نمره کسب ­شده توسط برنامه‎های کاربردی فاقد تبلیغات (میانه 3/9، IQR: 6/1-3/4) به‌طور معنی­داری بالاتر از برنامه­ های ­کاربردی دارای پیـام‌های تبلیغاتی (مـیانه: 2/9، IQR: 8/1-2/3)، (P=0/029) بود. در حوزه امتیازات عینی ابزار MARS، بیشترین نمره در حیطه کیفیت اطلاعات (0/4±3/9) و کمترین نمره در حیطه تعامل (0/5±3/5) به دست آمد. نتیجه‌گیری: مطالعه حاضر نشان داد که اپلیکیشن‌های طراحی شده برای کمک به پزشکان در تجویز آنتی‌بیوتیک‌ها، در کل از کیفیت قابل قبولی برخوردار هستند. در حوزه امتیازات عینی، امتیازات نسبتاً پایین‌تر کسب شده از قسمت تعامل، نشان داد که طراحان به این حیطه نسبت به کیفیت اطلاعات توجه کمتری داشته‌‌اند.   2 Introduction: One of the important challenges for physicians is the choice of the right antibiotics for various diseases. In this regard, a mobile application could be helpful. The main purpose of this study was to compare the quality of mobile applications designed for an antibiotic prescription to help physicians in choosing the right antibiotic. Method: In this study, English non-commercial apps from the Apple App Store and the Google Play Store were searched using relevant keywords. The apps were selected and independently scored by an infectious disease specialist and a pharmacist using the Mobile Application Rating Scale (MARS). Kendall’s coefficient of concordance was used to assess inter-rater agreement. The Kolmogorov-Smirnov test was used to verify the normal distribution of the quantitative variables. Spearman's rank-order correlation was applied to determine the relationship between MARS scores and quantitative background variables and Mann-Whitney U tests for dichotomous variables. - Results: In the initial search until August 2022, 13 apps were eligible for evaluation. The MARS score obtained by applications without in-app advertisements (median: 3.9, IQR: 3.4-6.1) was significantly higher than applications containing advertisements (median: 2.9, IQR: 2.3-8.1) (P=0.029). In the objective domain of MARS, The highest mean domain score belonged to the engagement section (3.9±0.4) and the lowest mean domain score belonged to the functionality section (3.5±0.5). Conclusion: This study indicated that apps designed to help physicians prescribe antibiotics meet acceptable criteria. Considering objective scores of MARS, lower scores in the “engagement” section demonstrated that designers have paid less attention to this section in comparison to the “information quality” section. 70 81 2023/02/52022/12/82023/01/32022/12/142023/01/82022/12/11 1401/9/20 2023/03/272023/03/72023/03/72023/05/242023/05/202023/03/13 1401/12/22 احسان نبوتی Ehsan Nabovati دانشیار انفورماتیک پزشکی، مرکز تحقیقات مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی کاشان، کاشان، ایران ایران enabovati@gmail.com سروش بیژنی Soroush Bijani داروساز، گروه فارماکولوژی و سم‌شناسی، دانشگاه علوم پزشکی زنجان، زنجان، ایران ایران soroush.bijani@gmail.com بهزاد بیژنی Behzad Bijani دانشیار بیماری‌های عفونی، مرکز تحقیقات میکروبیولوژی پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی قزوین، قزوین، ایران ایران dr.bijani@gmail.com Antibiotic mobile Applications Smartphone Clinical Decision Support MARS آنتی‌بیوتیک برنامه کاربردی همراه تلفن هوشمند تصمیم‌یار‌ MARS 1. 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Mobile health application to assist doctors in antibiotic prescription-an approach for antibiotic stewardship. Braz J Infect Dis 2017;21(6):660-4. doi: 10.1016/j.bjid.2017.08.002. ##14. Anthony Berauk VL, Murugiah MK, Soh YC, Chuan Sheng Y, Wong TW, Ming LC. Mobile health applications for caring of older people: review and comparison. Ther Innov Regul Sci 2018;52(3):374-82. https://doi.org/10.1177/2168479017725556##15. Nucci M, Landau M, Silveira F, Spector N, Pulcheri W. Application of the IDSA guidelines for the use of antimicrobial agents in neutropenic patients: impact on reducing the use of glycopeptides. Infect Control Hosp Epidemiol 2001; 22(10):651-3. doi: https://doi.org/10.1086/501839##16. Hand KS, Clancy B, Allen M, Mayes A, Patel Y, Latter SM.‘It makes life so much easier’—experiences of users of the MicroGuide™ smartphone app for improving antibiotic prescribing behaviour in UK hospitals: an interview study. JAC Antimicrob Resist 2021;3(3):dlab111. doi: 10.1093/jacamr/dlab111. ##17. Iglesias-Posadilla D, Gómez-Marcos V, Hernández-Tejedor A. Apps and intensive care medicine. Med. Med Intensiva 2017;41(4):227-236. doi: 10.1016/j.medin.2017.01.003. [Article in English, Spanish]##18. Jake-Schoffman DE, Silfee VJ, Waring ME, Boudreaux ED, Sadasivam RS, Mullen SP, et al. Methods for evaluating the content, usability, and efficacy of commercial mobile health apps. JMIR Mhealth Uhealth 2017;5(12):e190. doi: 10.2196/mhealth.8758.##19. Henson P, David G, Albright K, Torous J. Deriving a practical framework for the evaluation of health apps. Lancet Digit Health 2019;1(2):e52-e54. doi: 10.1016/S2589-7500(19)30013-5.##20. Mohamad UH: Comparison of User-Centered Design Antibiotic Resistance Awareness App to Existing Antibiotic-Related Mobile Applications. In Advances in Visual Informatics: 6th International Visual Informatics Conference; 2019 Nov 19–21; Proceedings 6: Bangi, Malaysia: Springer; 2019. p. 82-90. https://doi.org/10.1007/978-3-030-34032-2_8##21. Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M, Santo K, Knitza J, Machado GC, Schoeppe S, Bauereiß N. Validation of the mobile application rating scale (MARS). Plos one 2020;15(11):e0241480. https://doi.org/10.1371/journal.pone.0241480##22. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015;3(1):e27. doi: 10.2196/mhealth.3422##23. Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR Mhealth Uhealth 2015;3(3):e82. doi: 10.2196/mhealth.4328##24. Barzegari S, Sharifi Kia A, Bardus M, Stoyanov SR, GhaziSaeedi M, Rafizadeh M. 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بررسی نقش سیستم‌های پشتیبان تصمیم گیری بالینی در کاهش خطاهای پزشکی Investigating the Role of Clinical Decision Support Systems in Reducing Medical Errors 1 1 مقدمه: هدف پژوهش حاضر بررسی نقش سیستم‌های پشتیبان تصمیم گیری بالینی در کاهش خطاهای پزشکی از دیدگاه پزشکان و پرستاران بیمارستان‌های آموزشی درمانی شهرستان فردوس است. روش: پژوهش حاضر از نوع مطالعات توصیفی-مقطعی است که در سال 1400 و سه ماهه اول سال 1401 در دو بیمارستان آموزشی- درمانی شهرستان فردوس انجام شد. جامعه پژوهش پزشکان و پرستاران بودند. در این مطالعه از روش سرشماری، استفاده و جامعه پژوهش به عنوان نمونه پژوهش در نظر گرفته شد. ابزار گردآوری داده‌ها، پرسشنامه‌ آریایی و همکاران بود. پس از گردآوری پرسشنامه‏ ها، داده‏ ها با استفاده از روش‌های آمار توصیفی  مورد بررسی قرار گرفتند. نتایج: تعداد 42 نفر از کارکنان کادر درمان وارد مطالعه شدند. از دیدگاه پزشکان سیستم پشتیبان تصمیم می‌تواند منجر به کاهش احتمال بروز واکنش‌های آلرژی شدید و تداخل دارویی (60%)، امکان دسترسی سریع به سوابق دارویی بیمار (50%) و ثبت دستورات به صورت کامپیوتری (30%) ‏شود. از دیدگاه پرستاران حذف مشکلات مربوط به دست خط ناخوانای پزشکان (25%)، کاهش احتمال فراموش کردن تکرار آزمایش یا رادیولوژی (18/8%)، دسترسی سریع به اطلاعات روزآمد در حین کار (12/5%) و کاهش احتمال بروز آمبولی (9/4%) از مزایای سیستم‌های پشتیبان تصمیم گیری بالینی می باشند. نتیجه‌گیری: با توجه به اهمیت توسعه این سیستم‌ها در بیمارستان‌ها و همچنین سنجش آمادگی کادر درمان جهت پذیرش سیستم‌های پشتیبان تصمیم گیری، پیشنهاد می‌گردد آموزش‌های لازم در این زمینه ارائه گردد. 2 Introduction: This study aimed to investigate the role of clinical decision support systems in reducing medical errors from the perspective of physicians and nurses in the teaching and therapeutic hospitals. Method: This descriptive cross-sectional study was conducted in 2021-2022 in two teaching and therapeutic hospitals in Ferdows City, Iran. Physicians and nurses have participated in the research. In this study, the census method was used, and the research community was considered the research sample. The questionnaire of Ariyai et al. was used as a data collection tool. After collecting the questionnaires, the data were analyzed using descriptive statistics methods. Results: 42 medical staff were included in the study. From a physician’s point of view, decision support systems can be helpful by reducing the risk of severe allergic reactions and drug interactions (60%), quick access to patient records (50%), and computerized order registries (30%). From nurses' point of view, eliminating problems related to doctors' handwriting (25%), avoiding  disremember of repeating tests or imaging (18.8%), quick access to updated information during work (12.5%), and reducing the risk of embolism (9.4%) are the advantages of clinical decision support systems. Conclusion: due to the importance of developing a decision support system in hospitals and measuring the readiness of medical staff to adopt it, it is suggested that the necessary trainings be provided.   82 90 2023/02/52022/12/82023/01/32022/12/142023/01/82022/12/112023/01/10 1401/10/20 2023/03/272023/03/72023/03/72023/05/242023/05/202023/03/132023/02/13 1401/11/24 فائزه حاجی اسلام Faezeh Hajieslam کارشناس فناوری اطلاعات سلامت، دانشکده پیراپزشکی و بهداشت فردوس، دانشگاه علوم پزشکی بیرجند، بیرجند، ایران ایران hajieslami@yahoo.com زهره جوانمرد Zohreh Javanmard دانشجوی دکترای مدیریت اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران ایران zohreh.javanmard44@gmail.com Clinical Decision Support System (CDSS) Medical Error Physicians Nurses سیستم پشتیبان تصمیم‌گیری بالینی خطای پزشکی پزشکان پرستاران 1. Cao H, Stetson P, Hripcsak G. Assessing explicit error reporting in the narrative electronic medical record using keyword searching. AMIA Annu Symp Proc 2003;36(1-2):99-105.##2. Baloochi Beydokhti T, MohammadPour A, Shabab S, Nakhaee H. Related factors of medication errors and barriers of their reporting in the medical staff in hospitals of Gonabad University of Medical Sciences. Quarterly of the Horizon of Medical Sciences 2014;19(5):289-95. [In Persian]##3. Mosadeghrad AM, Isfahani P, Yousefinezhadi T. Medical errors in Iranian hospitals: systematic review. Tehran Univ Med J 2020;78(4):239-47. [In Persian]##4. Thomas EJ, Petersen LA. Measuring errors and adverse events in health care. J Gen Intern Med 2003;18(1):61-7. doi: 10.1046/j.1525-1497.2003.20147.x##5. Safdari R, Karami M, Mirzaee M, Rahimi A. A Systematic Review of Decision Support Systems: Effects on Health Care. Payavard Salamat 2013;7(1):56-70. [In Persian]##6. Jaspers MW, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc 2011;18(3):327-34. doi: 10.1136/amiajnl-2011-000094##7. Faizi K, Moghadasi A. Implications of Decision Support Systems in Decision Making of Managers. Management Studies in Development & Evolution 2005;12(45):139-61. [In Persian]##8. Agharezaei Z, Tofighi S, Nemati A, Agharezaei L, Bahaadinbeigi K. Surveying Kerman’s Afazalipour Hospital clinical and educational staff`s points of view about the clinical decision support system designed for reducing the possibility of pulmonary embolism and deep vein thrombosis. Hospital 2013;12(2):29-38. [In Persian]##9. Reporting medical errors Tabriz University of Medical Sciences [cited 10 Jun 2022] Available from: https://madanihosp.tbzmed.ac.ir/.##10. Moghaddasi H, Sheikhtaheri A, Hashemi N. Reducing medication errors: Role of computerized physician order entry system. Journal of Health Administration 2007;10(27):57-67. [In Persian]##11. Kaushal R, Barker KN, Bates DW. How can information technology improve patient safety and reduce medication errors in children's health care? Arch Pediatr Adolesc Med 2001;155(9):1002-7. doi: 10.1001/archpedi.155.9.1002##12. Jalalifar Fatemeh, Mehdi SM. Medication Process Analytics, Errors Recognition, and Contributory Factors in Creation of the Errors. Quarterly Journal of Nursing Management 2016;4(4):67 - 81. [In Persian].##13. Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013;20(3):470-6. doi: 10.1136/amiajnl-2012-001241##14. Ariaei M, Sarafi Nejad A, Kouti J, Mehdipour Z. Role of Clinical Decision Supporting Systems in Prevention of Medical Errors from the Perspective of Health Care Staff in University Hospitals of Kerman University of Medical Sciences, Iran. Health Informan Management 2013;9(5):712-23. [In Persian]##15. Kazemi A, Fors UG, Tofighi S, Tessma MK, Ellenius J. Physician order entry or nurse order entry? Comparison of two implementation strategies for a computerized order entry system aimed at reducing dosing medication errors. J Med Internet Res 2010;12(1): e1284. doi: 10.2196/jmir.1284##16. Khajouei R, Wierenga P, Hasman A, Jaspers MW. Clinicians satisfaction with CPOE ease of use and effect on clinicians’ workflow, efficiency and medication safety. International Journal of Medical Informatics. 2011;80(5):297-309. https://doi.org/10.1016/j.ijmedinf.2011.02.009##17. Niazkhani Z, Pirnejad H, Berg M, Aarts J. The impact of computerized provider order entry systems on inpatient clinical workflow: a literature review. J Am Med Inform Assoc 2009;16(4):539-49. doi: 10.1197/jamia.M2419##18. Rahimi B, Timpka T, Vimarlund V, Uppugunduri S, Svensson M. Organization-wide adoption of computerized provider order entry systems: a study based on diffusion of innovations theory. BMC Med Inform Decis Mak 2009;9(1):1-11. doi: 10.1186/1472-6947-9-52##19. Holden RJ. Physicians’ beliefs about using EMR and CPOE: in pursuit of a contextualized understanding of health IT use behavior. Int J Med Inform 2010;79(2):71-80. doi: 10.1016/j.ijmedinf.2009.12.003## ##

برنامه‌های کاربردی تلفن ‌همراه در حوزه سلامت ‌روان مورد استفاده در دوران همه‌گیری کووید-19: مروری سیستماتیک Mental Health Mobile Applications Used During the COVID-19 Pandemic: A systematic review 1 1 مقدمه: به منظور مقابله با مشکلات روحی-روانی ناشی از همه‏ گیری کووید-19، استفاده از برنامه‏ های کاربردی موبایل به طور فزاینده‎‏ای در بین کاربران افزایش یافته ‌است. هدف از مطالعه حاضر، بررسی برنامه‏‏ های کاربردی سلامت روان توسعه‌یافته یا مورداستفاده در دوران پاندمی کووید-19 می‏باشد. روش: در این مطالعه مروری جهت بازیابی مقالات مرتبط، کلیدواژه‌های "Covid-19" ، "health Mental"  و "applicationMobile " در پایگاه‌های اطلاعاتی Web of science، PubMed و Scopus بدون محدودیت زمانی تا تاریخ 12 آوریل 2022 جستجو شدند. پس از انتخاب مقالات طبق معیارهای ورود، ویژگی‏‏های برنامه‏ های کاربردی معرفی‌ شده، بررسی گردیدند. نتایج: در نهایت 25 مقاله به مطالعه حاضر وارد شدند.20 برنامه ‌کاربردی در دوران کرونا و 5 برنامه‌ کاربردی قبل از پاندمی توسعه یافته ‏اند. کاربر برنامه‌های کاربردی در چهار مورد کادردرمان، 16 مورد عموم مردم، یک مورد دانشجویان، یک مورد کودکان 9-3 سال، یک مورد بیماران کرونایی، یک مورد بیماران سرطانی و یک مورد مشترک بین کادر درمان و بیماران مبتلا به اختلال استرس می‏ باشند. اطلاعات مندرج در برنامه‏ های کاربردی بررسی شده نکات آموزشی جهت غلبه بر افکار منفی و مخرب و اطلاعات مرتبط با شیوه زندگی سالم بودند. همچنین امکان تماس با متخصصین روان در هشت برنامه‏ کاربردی وجود داشت. نتیجه‌گیری: نتایج مطالعه حاضر می‌تواند به عنوان منبعی جامع، جهت بررسی ویژگی‌ها و قابلیت‌های برنامه ‏های کاربردی سلامت روان در طی پاندمی‏ کرونا مورد استفاده قرار گیرد. پیشنهاد می‌شود از طریق شناسایی نقاط قوت و ضعف برنامه ‏های کاربردی سلامت روان، اقداماتی جهت توسعه برنامه‏ های کاربردی جامع‌تر و متناسب با سطح نیاز کاربران انجام ‌شود. 2 Introduction: During the COVID-19 pandemic, the use of mobile applications increased among people coping with mental health problems. This study aimed to review mobile applications developed or used during the COVID-19 pandemic. Method: In order to retrieve related articles, the keywords "COVID-19", "Mental health", and "Mobile application" were searched in Web of Science, PubMed, and Scopus databases without a time limit until April 12, 2022. After selecting the articles according to the entry criteria, the features of the introduced applications were checked. Results: Finally, 25 articles were included in the study, of which 20 applications were developed during the pandemic and five practical programs were already designed. Four applications were designed for medical staff, and 16 were designed for the general public, for each of students, children aged 3-9, COVID-19 patients, cancer patients, and both medical staff and patients with stress disorders one app was designed. The reviewed applications presented educational tips for overcoming negative and destructive thoughts, and getting information about a healthy lifestyle. Also, eight applications provided contractions between users and psychologists.  Conclusion: The results of this study can be used as a comprehensive source to examine the features and capabilities of mental health applications during the Coronavirus pandemic. It is suggested that by identifying the strengths and weaknesses of such applications, measures should be taken to develop more comprehensive applications that meet users' needs. 91 110 2023/02/52022/12/82023/01/32022/12/142023/01/82022/12/112023/01/102023/01/31 1401/11/11 2023/03/272023/03/72023/03/72023/05/242023/05/202023/03/132023/02/132023/03/11 1401/12/20 زهره جوانمرد Zohreh Javanmard دانشجوی دکترای تخصصی مدیریت اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران ایران zohreh.javanmard44@gmail.com فاطمه عامری Fatemeh Ameri دانشجوی کارشناسی ارشد فناوری اطلاعات سلامت، کمیته تحقیقات دانشجویی، گروه فناوری اطلاعات سلامت، دانشکده علوم پیراپزشکی، دانشگاه علوم پزشکی مشهد، مشهد. ایران ایران amerif4012@mums.ac.ir سارا گلمکانی Sara Golmakani دانشجوی کارشناسی پرستاری، کمیته تحقیقات دانشجویی، گروه پرستاری، دانشکده پیراپزشکی و بهداشت فردوس، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی بیرجند، بیرجند، ایران ایران sara.golmakani@gmail.com مائده حیدری Maede Heidary دانشجوی کارشناسی ارشد پرستاری سالمندی، کمیته تحقیقات دانشجویی، گروه آموزشی پرستاری داخلی جراحی، دانشکده پرستاری و مامایی، دانشگاه علوم پزشکی مشهد، مشهد، ایران ایران maede310@gmail.com نغمه نصرالهی Naghmeh Nasrolahi کارشناس پرستاری، گروه پرستاری، دانشکده پیراپزشکی و بهداشت فردوس، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی بیرجند، بیرجند، ایران ایران naghmenasrolahi@gmail.com عرفان رجبی Erfan Rajabi دانشجوی کارشناسی اتاق عمل، کمیته تحقیقات دانشجویی، گروه اتاق عمل، دانشکده پیراپزشکی و بهداشت فردوس، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی بیرجند، بیرجند، ایران ایران erfan.rjbi@gmail.com Mobile application Mental health COVID-19 Systematic Review برنامه کاربردی تلفن همراه سلامت روان کووید-19 مرور سیستماتیک 1. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine 2020; 382(13): 1-9. doi:10.1056/NEJMoa2001316##2. Farahati M. Psychological impacts of the spread of coronavirus in society. Social Impact Assessment 2020;1(2):207-25. [In Persian]##3. Alizadeh Birjandi Z, Ghoncheie M, Hokmabadi F, Mousaviyan SZ. The Relationship between Coronavirus Anxiety and its Consequences on Employees' Mental Health. Journal of Assessment and Research in Counseling and Psychology 2021;2(4):21-32. [In Persian]##4. Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res 2020;288:112954. doi: 10.1016/j.psychres.2020.112954.##5. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. 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