ArticleBMC infectious diseases2021
Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.
Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 30 citations in OpenAlex.
- Prediction of atelectasis inFrontiers in pediatrics · 2026Article
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- Development and multicentric external validation of a prognostic COVID-19 severity model based on thoracic CT.BMC medical informatics and decision making · 2025Article
- Pathogen spectrum and management strategies for opportunistic infections in lung cancer in the immunotherapy era: recent advances from fungi to mycobacteria.American journal of cancer research · 2025Review
- Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2.Radiologie (Heidelberg, Germany) · 2024Article
- Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung.Scientific reports · 2024Article
- Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.Scientific reports · 2024Observational
- A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.Entropy (Basel, Switzerland) · 2023Article
- Monkeypox detection using deep neural networks.BMC infectious diseases · 2023Article
- Radiomics analysis in medical imaging research.Journal of medical radiation sciences · 2023Article
- ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.Computers in biology and medicine · 2023Article
- The Clinical Utility of Molecular Imaging in COVID-19: An Update.Seminars in nuclear medicine · 2023Review
- Immunohematologic Biomarkers in COVID-19: Insights into Pathogenesis, Prognosis, and Prevention.Pathogens & immunity · 2023Review
- 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
- Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2021Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).
methodsIn this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
resultsFor the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model.
conclusionThe machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.
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