ArticleThe British journal of radiology2023
Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- A causal forest model integrating quantitative CT scores to predict benefit from flexible bronchoscopy in pediatric Mycoplasma pneumoniae pneumonia: a two-center retrospective study.Respiratory research · 2025Article
- AI-Based Quantitative HRCT for In-Hospital Adverse Outcomes and Exploratory Assessment of Reinfection in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
27 authors at 10 institutions in 8 countries.
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Abstract
objectiveWe aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.
methodsA deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.
resultsThe final population comprised 743 patients (mean age 65 ± 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739,
conclusionUtilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.
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