ArticleScientific reports2020
The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.
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 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection.Journal of clinical medicine · 2023Article
- 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.Journal of digital imaging · 2023Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA).Diagnostics (Basel, Switzerland) · 2022Article
- Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates.Trends in molecular medicine · 2022Review
- Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic.World journal of radiology · 2021Review
- Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.BMC infectious diseases · 2021Article
- Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.Scientific reports · 2021Article
- A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2021Article
- Review
- Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.Computers in biology and medicine · 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
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10 authors.
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Abstract
To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.
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