ArticleBMC infectious diseases2022
Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.The British journal of radiology · 2022Trial
- A Review of the Role of Artificial Intelligence in Patients on Extracorporeal Membrane Oxygenation: A Scoping Review Study.Health science reports · 2026Review
- Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study.JMIR formative research · 2024Article
- Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions.Archives of academic emergency medicine · 2024Review
- Human-AI Symbiosis: A Path Forward to Improve Chest Radiography and the Role of Radiologists in Patient Care.Radiology · 2024Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study.Frontiers in medicine · 2023Article
- Review
Corrections and comments
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Authors and funding
22 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAirspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.
methodsThis retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.
resultsOverall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).
conclusionThe deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.
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