ArticleKorean journal of radiology2021
Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.
Article in Korean journal of radiology, 2021. 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.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity.La Radiologia medica · 2022Pooled it
- Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study.Medical physics · 2022Trial
- Enhancing the Study of Long-Term Pulmonary Sequelae After COVID-19: Methodological Perspectives on Imaging and Clinical Integration.Korean journal of radiology · 2026Article
- AI-Based Quantitative HRCT for In-Hospital Adverse Outcomes and Exploratory Assessment of Reinfection in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients.Medical & biological engineering & computing · 2022Article
- Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings.Preventive medicine reports · 2022Article
- Predicting the Disease Severity of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Looking Ahead to 2022 for theKorean journal of radiology · 2022Article
- A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.Diagnostics (Basel, Switzerland) · 2021Review
- Rapid diagnosis of SARS-CoV-2 pneumonia on lower respiratory tract specimens.BMC infectious diseases · 2021Article
- CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
objectiveTo develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND
methodsClinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.
resultsAmong 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70,
conclusionCT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
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Registered trials
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