Innovative solution for SARS-COV2 epidemic management

Stanciu, Silviu; Gurzun, Magdalena; Iancu, Diana Camelia; Gheorghiță, Valeriu; Lupașcu, Cristian; Gugulea, George; Ropot, Armand

doi:10.55453/rjmm.2021.124.3.10

Cited by 3 publications

(1 citation statement)

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“…These mobile apps demonstrated optimal performance and achieved an accuracy of >92%. By automatically extracting the features using manual feature selection, the bias in the results of diagnostic models was eliminated [ 18 , 29 , 45 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

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

Gheisari,

Ghaderzadeh,

et al. 2024

JMIR Mhealth Uhealth

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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.

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Section: Resultsmentioning

confidence: 99%

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

Gheisari,

Ghaderzadeh,

et al. 2024

JMIR Mhealth Uhealth

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A Systematic Review of Health Management Mobile Applications in COVID-19 Pandemic: Features, Advantages, and Disadvantages

Garavand,

Ameri,

Salehi

et al. 2024

BioMed Research International

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Introduction. With the increasing accessibility of smartphones, their use has been considered in healthcare services. Mobile applications have played a pivotal role in providing health services during COVID-19. This study is aimed at identifying the features, advantages, and disadvantages of health management mobile applications during COVID-19. Methods. This systematic review was conducted in PubMed, Scopus, and Web of Science using the related keywords up to November 2021. The original articles in English about the health management mobile applications in COVID-19 were selected. The study selection was done by two researchers independently according to inclusion and exclusion criteria. Data extraction was done using a data extraction form, and the results were summarized and reported in related tables and figures. Results. Finally, 12 articles were included based on the criteria. The benefits of mobile health applications for health management during COVID-19 were in four themes and 19 subthemes, and the most advantages of the application were in disease management and the possibility of recording information by users, digital tracking of calls, and data confidentiality. Furthermore, the disadvantages of them have been presented in two themes and 14 subthemes. The most common disadvantages are reduced adherence to daily symptom reports, personal interpretation of questions, and result bias. Conclusion. The study results showed that mobile applications have been effective in controlling the prevalence of COVID-19 by identifying virus-infested environments, identifying and monitoring infected people, controlling social distancing, and maintaining quarantine. It is suggested that usability, ethical and security considerations, protection of personal information, and privacy of users be considered in application design and development.

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

Campusano¹,

Taami²,

Sadeghsalehi³

et al. 2022

Preprint

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BACKGROUND In the modern world, mobile applications are essential to human advancement and pandemic control is no exception. The use of mobile applications and technology for the detection and diagnosis of COVID-19 disease has been the subject of numerous investigations. Objective: Since no thorough analysis of the COVID-19 epidemic prevention has been done using mobile applications. Due to this gap, the current study thoroughly covers the many uses of mobile applications for COVID-19 detection and diagnosis. OBJECTIVE Since no thorough analysis of the COVID-19 epidemic prevention has been done using mobile applications. Due to this gap, the current study thoroughly covers the many uses of mobile applications for COVID-19 detection and diagnosis. METHODS A search of five major research databases (ScienceDirect, Scopus, PubMed, Web of Science, IEEE) found 535 studies, among which 42 related to the diagnosis and detection of patients suspected of having COVID-19. RESULTS Mobile applications can be categorized into five areas based on the content of these studies: contact tracing, data gathering, data visualization, artificial intelligence-based methods, rule- and guideline-based methods, and data transformation. Patients with COVID-19 have been identified using mobile applications employing a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. The majority of studies concentrated on using artificial intelligence (AI) methods to identify people who might have COVID-19. Additionally, compared to other data types, symptoms, cough sounds, and radiological images were used more frequently. CONCLUSIONS Mobile applications could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and early identification of suspected cases. These technologies can work in conjunction with the Internet of Things (IoT), cloud storage, 5G, and cloud computing.

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Innovative solution for SARS-COV2 epidemic management

Cited by 3 publications

References 0 publications

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

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

A Systematic Review of Health Management Mobile Applications in COVID-19 Pandemic: Features, Advantages, and Disadvantages

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

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