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.

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

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. In developing countries, most primary healthcare centers do not have electronic medical records, and outpatient healthcare centers are rarely connected electronically to their reference laboratories.9'13 Even developed countries, which are pioneers in this domain, have not fully addressed the challenges related to interoperability.14"17 Paper-based communications, where patients themselves occasionally carry laboratory information, waste a great deal of time and energy commuting between outpatient centers and laboratories outside these centers.18-19 This imposes heavy delay and backlog on the transfer oftest results, and physicians cannot always have timely access to these results. If HCPs fail to follow-up test results, patients are exposed to a heightened risk of misdiagnosis or delayed treatment, and this leads to unfavorable treatment outcomes and threatens the quality of care and patient safety and satisfaction. [...]it is essential to receive and follow-up laboratory results in order to improve patient safety and the quality of care.20-21 In some cases, information exchange between physicians and the laboratory (both requests for tests and retrieval of results) is performed via email, fax, special landlines, and printers;in addition to suffering from the mentioned problems, these methods are not documented or reliable.22'24 Laboratory service process errors are more important in offices because these healthcare centers are most frequently visited by patients25 and, as such, a large number of tests are requested and the process oftest is more complicated in offices.26 Accordingly, the present study aimed to overcome the problems associated with paper-based information exchange between laboratories and offices, improve outpatient safety and the quality of care, and help save time and energy by developing an electronic information exchange system27for laboratories and offices,28 especially in the COVID-19 pandemic when electronic information exchange could be effective.29-30 Method In this applied study, the processes related to information transfer between diagnostic laboratories and offices were examined, and a list of physicians' and laboratories' needs was drawn up based on a review of sources (books and articles) and surveying physicians (n=5) and laboratories (n=5). Since there were eight participants, the score of each question could range from 0 to 16.

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.

Applications, features and key indicators for the development of Covid-19 dashboards: A systematic review study.

Introduction: Interactive dashboards can collect data from various information sources and be used nationally and internationally. These information systems have played an important role in managing and controlling epidemic diseases, especially Covid-19. This study aimed to identify the applications, features, and key indicators of advanced dashboards in Covid-19. Method: The present article is a systematic review study that searched the PubMed, Scopus, and ISI web of sciences databases in 2021 by combining the relevant keywords. After applying the inclusion and exclusion criteria and selecting articles, data collection was prepared using a data collection form. Data analysis was performed using the content analysis method. Results: Out of 171 articles retrieved, 19 were included in the study for review by applying inclusion and exclusion criteria in the first stage. The most important data sources for the studied dashboards included general online, national, and hospital databases. Monitoring and tracking in the target community and resource management (hospital and public) are the most important issues in Covid-19 dashboards. The study showed that KPIs in 5 main categories of indicators related to hospital beds, clinical data in the hospital, diagnostic and therapeutic measures of hospitals, epidemiological data at the level community, and follow-up indicators of Covid-19 studies were worldwide. Conclusion: Considering the technological advances at the world level and the large amount of data produced, one of the effective solutions for managing and controlling epidemic and pandemic conditions and diseases is the rapid development of interactive dashboards; Therefore, it is suggested that health officials and policymakers, in addition to developing and updating the existing dashboards in the field of Covid-19, developing the dashboard immediately in case of similar conditions.

Telehealth in Management of COVID-19 Pandemic: A Scoping Review

COVID-19 has created major health-related, economic, and social challenges in societies, and its high contagion has dramatically altered access to healthcare. COVID-19 management can be improved by the use of telehealth. This study aimed to examine different telehealth technologies in the management of COVID-19 disease in the domains of surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up and investigate the challenges to the application of telehealth in COVID-19 management. This scoping review was conducted based on Arksey and O'Malley's framework. Searches were performed in Web of Science, PubMed, and Scopus databases to examine the evidence on the effectiveness of telehealth in COVID-19 management. Eventually, 36 articles were selected based on the inclusion criteria. The majority of these studies (33%) were conducted in China. Most services offered via telehealth focused on surveillance, tracking, and follow-up, in that order. Moreover, the most frequently used technologies were social networks, web-based apps, and mobile apps, respectively. The use of telehealth in COVID-19 disease management plays a key role in surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up. [ FROM AUTHOR] Copyright of Acta Medica Iranica is the property of Tehran University of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

Corrigendum to "Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review".

[This corrects the article DOI: 10.1155/2021/6677314.].

X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.

PURPOSE: Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases. METHOD: This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity. RESULT: The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19. CONCLUSION: X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.

Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study.

BACKGROUND: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. OBJECTIVE: Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm. METHODS: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. RESULTS: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.

Introduction: The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result: This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion: The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.