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

Autor:	Gheisari M; Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China.; Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India, Ghaderzadeh M; School of Nursing and Health Sciences of Boukan, Urmia University of Medical Sciences, Urmia, Iran., Li H; Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China., Taami T; Florida State University, Tallahassee, FL, United States., Fernández-Campusano C; Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago, Chile., Sadeghsalehi H; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Tehran, Iran., Afzaal Abbasi A; Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy.
Jazyk:	angličtina
Zdroj:	JMIR mHealth and uHealth [JMIR Mhealth Uhealth] 2024 Feb 22; Vol. 12, pp. e44406. Date of Electronic Publication: 2024 Feb 22.
DOI:	10.2196/44406
Abstrakt:	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. (©Mehdi Gheisari, Mustafa Ghaderzadeh, Huxiong Li, Tania Taami, Christian Fernández-Campusano, Hamidreza Sadeghsalehi, Aaqif Afzaal Abbasi. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 22.02.2024.)
Databáze:	MEDLINE
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