ArticleScientific reports2020
Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis.Journal of medical imaging and radiation sciences · 2023Pooled it
- A meta-analysis on the risk factors adjusted association between cardiovascular disease and COVID-19 severity.BMC public health · 2021Pooled it
- Development and Validation of a CT-based Deep Transfer Learning Radiomic Model for Predicting Post-COVID-19 Pulmonary FibrosisCurrent medical imaging · 2026Article
- Consolidation-to-ground-glass opacity ratio on chest CT as a prognostic marker for critical outcomes in COVID-19: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- Patterns of restricted TCR usage following SARS-CoV-2 vaccination and severe disease.Frontiers in immunology · 2025Article
- Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.Journal of clinical medicine · 2024Article
- Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning.European journal of translational myology · 2023Article
- Tools and factors predictive of the severity of COVID-19.Global health & medicine · 2023Review
- Increased lactate dehydrogenase reflects the progression of COVID-19 pneumonia on chest computed tomography and predicts subsequent severe disease.Scientific reports · 2023Article
- Serum gasdermin D levels are associated with the chest computed tomography findings and severity of COVID-19.Respiratory investigation · 2022Article
- Leukocyte glucose index as a novel biomarker for COVID-19 severity.Scientific reports · 2022Article
- SARS-Cov-2 pneumonia phenotyping on imaging exams of patients submitted to minimally invasive autopsy.Annals of translational medicine · 2022Article
- Pandemic strategies with computational and structural biology against COVID-19: A retrospective.Computational and structural biotechnology journal · 2022Review
- Application of artificial intelligence in COVID-19 medical area: a systematic review.Journal of thoracic disease · 2021Review
- Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.Japanese journal of radiology · 2021Article
- Clinical and radiological characteristics of COVID‑19 patients without comorbidities : A single-center study.Wiener klinische Wochenschrift · 2021Article
- The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.Informatics in medicine unlocked · 2021Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm
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