ReviewMedical devices (Auckland, N.Z.)2025
Comparing Ultrasound, Chest X-Ray, and CT Scan for Pneumonia Detection.
Review in Medical devices (Auckland, N.Z.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Externally Validated Deep Learning Analysis of Chest Radiographs for Differentiating COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2026Article
- Optimizing transducer choice for the detection of alveolar-interstitial syndrome in dogs and cats: an evidence-based review.Frontiers in veterinary science · 2026Review
- The Usefulness and Limitations of Radiologic Findings for Diagnosis of Infectious Diseases: A Call for Antimicrobial Stewardship.Infection & chemotherapy · 2025Review
- Assessing the Reliability of Lung Ultrasound in Neonatal Acute Care Setting: A Systematic Review Protocol.Biomedicine hubArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The clinical methods of diagnosing pneumonia have been for many years chest X-rays (CXR) and computed tomography (CT) scans. However, a relatively new modality that is promising, efficient, and cheap is the lung ultrasound (LUS). The scope of this systematic review focuses on evaluating the diagnostic performance of LUS, CXR, and CT for diagnosing pneumonia. Objective: The first aim of this review is to assess the diagnostic accuracy of lung ultrasound in comparison with chest X-rays and CT scans. Methods: PubMed, Cochrane, Embase, and Scopus were electronically searched without date and language limitations were set. Inclusion criteria covered only those diagnostic studies that involved comparing lung ultrasound, chest X-ray, and CT scans in human beings with suspected pneumonia. Both abstraction and quality of studies were assessed by two researchers and the quality was assessed using PRISMA and QUADAS-2 checklists. Results: There were 13 empirical studies included in the review which referred to different patients and contexts. Although none of the imaging techniques could achieve both high sensitivity and high specificity, CT scans produced the highest sensitivity, with values higher than 95% on all occasions, and specificities ranging between 80% and 90%. Lung US was most helpful in pediatric and acute care populations, whereas CT was preferred when there was uncertainty regarding the presentation. Conclusion: Lung ultrasound is a very sensitive, specific, practical method of subsequent lung examination; it is useful in environments that need fast bedside assessment and for patients particularly vulnerable to radiation exposure, such as children. Even though chest X-ray remains valuable in excluding pneumonia in outpatient practices, it lacks sensitivity and is therefore less useful in diagnosing early disease. Computed tomography scans are the most accurate type of scans but should only be used in the more severe interventions due to cost and radiation exposure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.