Current Diagnostic Techniques for Pneumonia: A Scoping Review.

COVID-19 community-acquired pneumonia diagnostic radiography medical diagnosis non-invasive measurements

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
01 Jul 2024
Historique:
received: 29 05 2024
revised: 22 06 2024
accepted: 28 06 2024
medline: 13 7 2024
pubmed: 13 7 2024
entrez: 13 7 2024
Statut: epublish

Résumé

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

Identifiants

pubmed: 39001069
pii: s24134291
doi: 10.3390/s24134291
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : HEC Indigenous Scholarship Phase V
ID : 518-74863-2EG5-064

Auteurs

Kehkashan Kanwal (K)

College of Speech, Language, and Hearing Sciences, Ziauddin University, Karachi 75000, Pakistan.

Muhammad Asif (M)

Faculty of Computing and Applied Sciences, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan.

Syed Ghufran Khalid (SG)

Department of Engineering, Faculty of Science and Technology, Nottingham Trent University, Nottingham B15 3TN, UK.

Haipeng Liu (H)

Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK.

Aisha Ghazal Qurashi (AG)

Jubilee Healthcare Centre, Coventry CV1 3GB, UK.

Saad Abdullah (S)

School of Innovation, Design and Engineering, Mälardalen University, 721 23 Västerås, Sweden.

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Classifications MeSH