ArticleEuropean radiology2021
Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04318366 (Study to Characterize Patients With SARS-Cov-2 Infection and to Create a Biobank to Identify Predictors of Disease Severity, Mortality and Treatment Response), which is not on this map. Cited by 74 papers, 3 of them syntheses that pooled it.
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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.
Study to Characterize Patients With SARS-Cov-2 Infection and to Create a Biobank to Identify Predictors of Disease Severity, Mortality and Treatment Response
Who cites it
74 citing papers in PubMed, 3 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Pooled it
- Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- 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
- AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.Journal of imaging informatics in medicine · 2026Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Article
- New scoring system combining WHO ordinal scale and MASCC score in predicting mortality of COVID-19 patients with malignancy and neutropenia: a retrospective cohort study.BMC infectious diseases · 2025Article
- Radiologic severity index can be used to predict mortality risk in patients with COVID-19.Tuberkuloz ve toraks · 2024Article
- Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.Healthcare (Basel, Switzerland) · 2024Article
- Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.Quantitative imaging in medicine and surgery · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.Scientific reports · 2024Article
- Characteristics of patients with COVID-19 who have deteriorating chest X-ray findings within 48 h: a retrospective cohort study.Scientific reports · 2023Article
- An AI-Based Image Quality Control Framework for Knee Radiographs.Journal of digital imaging · 2023Article
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media.Sensors (Basel, Switzerland) · 2023Review
- Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2023Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.
methodsThis retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses.
resultsSix hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52-75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 - 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35-4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease.
conclusionAI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19.
trial registrationClinicalTrials.gov NCT04318366 ( https://clinicaltrials.gov/ct2/show/NCT04318366 ). KEY POINTS: • AI system-based score ≥ 30 and a RALE score ≥ 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. • Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. • The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.
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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.