ArticleBMJ open2020
Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it, 133 citations in OpenAlex.
- Thoracic imaging tests for the diagnosis of COVID-19.The Cochrane database of systematic reviews · 2022Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Trial
- Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models.Narra J · 2025Article
- Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.Biomedical engineering letters · 2025Article
- Evaluating the utility of chest x-rays for non-traumatic chest pain in Australia- a retrospective cohort study.Emergency radiology · 2025Article
- Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions.Scientific reports · 2025Article
- Digitalised multidisciplinary conferences effectively identify and prevent imaging-related medical error in intensive care patients during the COVID-19 pandemic.Scientific reports · 2025Article
- X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).Journal of imaging · 2024Article
- Effects of dog cardiac orientation on vertebral heart score measurements in different thoracic types.Veterinary world · 2024Article
- COVID-19 severity detection using chest X-ray segmentation and deep learning.Scientific reports · 2024Article
- Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.BMC medical imaging · 2024Article
- Point-of-care ultrasound use in COVID-19: a narrative review.Annals of translational medicine · 2024Review
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Emergency department CT examinations demonstrate no evidence of early viral circulation at the start of the COVID-19 pandemic-a multicentre epidemiological study.Insights into imaging · 2024Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity.Scientific reports · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo identify the diagnostic accuracy of common imaging modalities, chest X-ray (CXR) and CT, for diagnosis of COVID-19 in the general emergency population in the UK and to find the association between imaging features and outcomes in these patients.
designRetrospective analysis of electronic patient records.
settingTertiary academic health science centre and designated centre for high consequence infectious diseases in London, UK.
participants1198 patients who attended the emergency department with paired reverse transcriptase PCR (RT-PCR) swabs for SARS-CoV-2 and CXR between 16 March and 16 April 2020.
main outcome measuresSensitivity and specificity of CXR and CT for diagnosis of COVID-19 using the British Society of Thoracic Imaging reporting templates. Reference standard was any RT-PCR positive naso-oropharyngeal swab within 30 days of attendance. ORs of CXR in association with vital signs, laboratory values and 30-day outcomes were calculated.
resultsSensitivity and specificity of CXR for COVID-19 diagnosis were 0.56 (95% CI 0.51 to 0.60) and 0.60 (95% CI 0.54 to 0.65), respectively. For CT scans, these were 0.85 (95% CI 0.79 to 0.90) and 0.50 (95% CI 0.41 to 0.60), respectively. This gave a statistically significant mean increase in sensitivity with CT of 29% (95% CI 19% to 38%, p<0.0001) compared with CXR. Specificity was not significantly different between the two modalities.CXR findings were not statistically significantly or clinically meaningfully associated with vital signs, laboratory parameters or 30-day outcomes.
conclusionsCT has substantially improved diagnostic performance over CXR in COVID-19. CT should be strongly considered in the initial assessment for suspected COVID-19. This gives potential for increased sensitivity and considerably faster turnaround time, where capacity allows and balanced against excess radiation exposure risk.
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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.