Pediatric lung imaging features of COVID‐19: A systematic review and meta‐analysis
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Rationale: Pediatric COVID‐19 studies have been mostly restricted to case reports and small case series, which have prevented the identification of specific pediatric lung disease patterns in COVID‐19. The overarching goal of this systematic review and meta‐analysis is to provide the first comprehensive summary of the findings of published studies thus far describing COVID‐19 lung imaging data in the pediatric population. Methods: A systematic literature search of PubMed was performed to identify studies assessing lung‐imaging features of COVID‐19 pediatric patients (0–18 years). A single‐arm meta‐analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI). Results: A total of 29 articles ( n = 1026 children) based on chest computerized tomography (CT) images were included. The main results of this comprehensive analysis are as follows: (1) Over a third of pediatric patients with COVID‐19 (35.7%, 95% CI: 27.5%–44%) had normal chest CT scans and only 27.7% (95% CI: 19.9%–35.6%) had bilateral lesions. (2) The most typical pediatric chest CT findings of COVID‐19 were ground‐glass opacities (GGO) (37.2%, 95% CI: 29.3%–45%) and the presence of consolidations or pneumonic infiltrates (22.3%, 95% CI: 17.8%–26.9%). (3) The lung imaging findings in children with COVID‐19 were overall less frequent and less severe than in adult patients. (4) Typical lung imaging features of viral respiratory infections in the pediatric population such as increased perihilar markings and hyperinflation were not reported in children with COVID‐19. Conclusion: Chest CT manifestations in children with COVID‐19 could potentially be used for early identification and prompt intervention in the pediatric population.
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An under-represented population or age group in one hospital or region might be highly represented in another, such as children who may be differentially affected by COVID-19.
“An under-represented population or age group in one hospital/region might be highly represented in another region, such as children, who might be differentially affected by COVID-19, including disease manifestations in lung imaging46.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [46]
A prior mortality prediction model for SARS-CoV-2 patients used clinical features but was limited in number of modalities and scale.
“A notable example is a mortality prediction model in patients infected with SARS-COV-2 that uses clinical features, albeit limited in terms of number of modalities and scale46.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [46]
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- Authors
- Nino, G., Zember, J., Sanchez‐Jacob, R., Gutierrez, M. J., Sharma, K., & Linguraru, M. G.
- Affiliations
- Division of Pediatric Pulmonary and Sleep Medicine Children's National Hospital Washington District of Columbia USA
- Children's National (US)
- Department of Pediatrics George Washington University School of Medicine Washington District of Columbia USA
- Johns Hopkins University (US)
- Venue
- Pediatric Pulmonology
- Publisher
- Wiley
- Published
- 2020-11-02
- Volume
- 56
- Issue
- 1
- Pages
- 252-263
- ISSN
- 1099-0496 (ISSN-L)
- 8755-6863
- PMC
- PMC8287438
- OpenAlex
- W3085026028
- Cited by
- 65
- References
- 56
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 3 · 2023: 13 · 2022: 18 · 2021: 21 · 2012: 1
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Pediatric Pulmonology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (HL141237 and, HL145669)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Neurology › Long-Term Effects of COVID-19
- Keywords
- COVID-19 pandemic, pediatric COVID-19, lung imaging, chest CT, ground-glass opacities, lung consolidation, bilateral lung lesions, hyperinflation, respiratory viral infections, children
- MeSH terms
- COVID-19, Adolescent, Adult, Child, Child, Preschool, Female, Humans, Infant, Lung, Male, Sensitivity and Specificity, Radiography, Thoracic, Tomography, X-Ray Computed
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