Ultrasonography in the Diagnosis of Bronchiolitis: Evaluation of Effectiveness and Application in Clinical Practice
Introduction and Purpose This study investigates the effectiveness and clinical applicability of lung ultrasound (LUS) in diagnosing acute bronchiolitis. Bronchiolitis is challenging to diagnose due to symptom overlap with other respiratory infections and varying clinical presentations. While co...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kazimierz Wielki University
2024-12-01
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| Series: | Journal of Education, Health and Sport |
| Subjects: | |
| Online Access: | https://apcz.umk.pl/JEHS/article/view/55983 |
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| Summary: | Introduction and Purpose
This study investigates the effectiveness and clinical applicability of lung ultrasound (LUS) in diagnosing acute bronchiolitis. Bronchiolitis is challenging to diagnose due to symptom overlap with other respiratory infections and varying clinical presentations. While conventional diagnosis relies on patient history and physical examination, LUS has emerged as a promising, non-invasive tool that may improve diagnostic accuracy and provide valuable insights into disease severity without the radiation risks associated with X-rays.
Material and Methods
This review is based on articles from the PubMed database, covering the years 2012-2024, using keywords: bronchiolitis, LUS, lung ultrasound.
Results
The study found that LUS demonstrated high sensitivity and specificity for identifying bronchiolitis-related lung abnormalities, including areas of consolidation and interstitial involvement. A strong association was observed between ultrasound scores and clinical severity, with elevated scores correlating with increased need for respiratory support. LUS was more effective than conventional chest X-rays for detecting consolidations over 1 cm and predicting oxygenation issues in critically ill patients.
Conclusions
LUS proves to be a valuable diagnostic tool for bronchiolitis, providing a safe, radiation-free alternative to traditional imaging and yielding reliable information on disease severity. Its ease of use, combined with its predictive value in identifying children at risk for respiratory complications, highlights LUS as a practical method for bedside assessment. Moreover, advancements in AI-supported LUS analysis hold potential for reducing operator dependency, enhancing diagnostic consistency, and improving workflow efficiency in emergency settings. Integrating LUS into routine practice could thus enhance care for pediatric bronchiolitis patients, supporting more accurate, timely diagnoses and targeted treatment interventions.
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| ISSN: | 2391-8306 |