ArticleAnnals of translational medicine2021
A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Prevalence and clinical outcomes of pleural effusion in COVID-19 patients: A systematic review and meta-analysis.Journal of medical virology · 2022Pooled it
- Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review.PloS one · 2020Pooled it
- Development and validation of a disease-syndrome integrated assessment model for AECOPD severity: protocol for a nationwide multicentre cross-sectional study.BMJ open respiratory research · 2026Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm.Journal of clinical medicine · 2024Article
- Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning.Journal of imaging · 2024Article
- Derivation and external validation of a nomogram predicting the occurrence of severe illness among hospitalized coronavirus disease 2019 patients: a 2020 Chinese multicenter retrospective study.Journal of thoracic disease · 2023Article
- Development and Validation of an Acute Respiratory Distress Syndrome Prediction Model in Coronavirus Disease 2019: Updated Lung Injury Prediction Score.Mayo Clinic proceedings · 2023Article
- Relation of impulse oscillometry and spirometry with quantitative thorax computed tomography after COVID-19 pneumonia.Revista da Associacao Medica Brasileira (1992) · 2023Article
- MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.Signal processing. Image communication · 2022Article
- Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.International journal of environmental research and public health · 2022Article
- Human Identical Sequences, hyaluronan, and hymecromone ─ the new mechanism and management of COVID-19.Molecular biomedicine · 2022Review
- A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients.BMC medical informatics and decision making · 2022Article
- Hymecromone: a clinical prescription hyaluronan inhibitor for efficiently blocking COVID-19 progression.Signal transduction and targeted therapy · 2022Article
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics.Future virology · 2022Article
- Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.SN computer science · 2022Article
- Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images.Biomedical engineering online · 2021Article
- Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.Clinical imaging · 2021Article
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.
methodsOne hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.
resultsIn comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOI
conclusionsAlthough quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.
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