ArticleDiagnostics (Basel, Switzerland)2021
Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- AI-Based Quantitative HRCT for In-Hospital Adverse Outcomes and Exploratory Assessment of Reinfection in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence algorithms based approach in evaluating COVID-19 patients and management.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2025Article
- Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms.Diagnostics (Basel, Switzerland) · 2023Article
- Chest CT in covid-19 pneumonia's follow-up: A 30 patients case series.Annals of medicine and surgery (2012) · 2022Article
- CT of Post-Acute Lung Complications of COVID-19.Radiology · 2021Review
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Authors and funding
10 authors at 3 institutions in 1 country.
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Abstract
The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (
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