ArticleScientific reports2021
Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- NDL-Net: A Hybrid Deep Learning Framework for Diagnosing Neonatal Respiratory Distress Syndrome From Chest X-Rays.IEEE open journal of engineering in medicine and biology · 2025Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
- Early detection of heart failure using in-patient longitudinal electronic health records.PloS one · 2024Article
- Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs.Journal of clinical medicine · 2023Article
- Role of Imaging in the Management of Patients with SARS-CoV-2 Lung Involvement Admitted to the Emergency Department: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- An Artificial Neural Network for Nasogastric Tube Position Decision Support.Radiology. Artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid .
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Registered trials
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