文献类型：期刊文献

英文题名：Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

作者：Gheisari, Mehdi[1,2];Ghaderzadeh, Mustafa[3];Li, Huxiong[1];Taami, Tania[4];Fernandez-Campusano, Christian[5];Sadeghsalehi, Hamidreza[6];Abbasi, Aaqif Afzaal[7]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing, Peoples R China;[2]Saveetha Sch Engn, Inst Comp Sci & Engn, Dept Cognit Comp, Chennai, India;[3]Urmia Univ Med Sci, Sch Nursing & Hlth Sci Boukan, Kurdistan Blv Boukan, Orumiyeh 5951715161, Iran;[4]Florida State Univ, Tallahassee, FL USA;[5]Univ Santiago de Chile, Fac Ingn, Dept Ingn Elect, Santiago, Chile;[6]Fac Adv Technol Med, Dept Neurosci, Tehran, Iran;[7]Univ Palermo, Dept Earth & Marine Sci, Palermo, Italy

年份：2024

卷号：12

起止页码：e44406

外文期刊名：JMIR MHEALTH AND UHEALTH

收录：SCI-EXPANDED(收录号：WOS:001177656500001)、、Scopus(收录号：2-s2.0-85185728595)、WOS

语种：英文

外文关键词：COVID-19; detection; diagnosis; internet of things; cloud computing; mobile applications; mobile app; mobile apps; artificial intelligence: AI; mobile phone; smartphone

外文摘要：Background: In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. Objective: With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. Methods: In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Results: Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. Conclusions: Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.