ArticleFrontiers in medicine2022
Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2025Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.Computational and structural biotechnology journal · 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
- Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI-processed radiological parameter upon admission: a multicentre study.Scientific reports · 2024Article
- A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.PloS one · 2023Article
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.
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