Diagnostic accuracy of various imaging modalities for children with pneumonia: a systematic review and network meta-analysis

儿童肺炎各种影像学检查诊断准确性:系统评价和网络荟萃分析

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Abstract

BACKGROUND: Pneumonia remains the predominant cause of childhood mortality and morbidity globally. While various imaging modalities have been employed for paediatric pneumonia diagnosis, the diagnostic accuracy remains inadequately characterised. OBJECTIVE: To systematically evaluate and compare the diagnostic accuracy of available imaging modalities for paediatric pneumonia through both diagnostic test accuracy (DTA) meta-analyses and network meta-analysis (NMA). METHODS: PubMed, Embase, Cochrane Library and Web of Science were searched up to March 2025. The risk of bias was graded using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Diagnostic accuracy measures were pooled using random-effects DTA meta-analyses, while relative diagnostic performance was compared through NMAs. RESULTS: 81 studies published in 22 countries since 2008 were included, with a total of 34 625 children. In most of the studies, there was an unclear risk of bias. When clinical examination served as reference test, lung ultrasound demonstrated high diagnostic accuracy with sensitivity of 0.91 and specificity of 0.93. NMAs showed superior overall diagnostic performance of computer-aided chest radiography compared to lung ultrasound across all indexes except specificity, where there was no difference in sensitivity or specificity between the two. Meta-regression identified study design and pneumonia type as significant modifiers of diagnostic sensitivity. CONCLUSION: This comprehensive analysis provides robust evidence supporting the clinical utility of computer-aided chest radiography and lung ultrasound for paediatric pneumonia diagnosis. However, insufficient evidence precludes definitive conclusions regarding other computer-aided modalities. Future high-quality comparative studies are needed to validate these findings in diverse clinical settings and evaluate emerging imaging technologies.

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