ArticleEuropean journal of radiology2021
Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19.
Article in European journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department.The western journal of emergency medicine · 2024Article
- Article
- An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.ERJ open research · 2022Article
- High resolution computed tomography texture analysis identifies patients at risk of pulmonary fibrosis after COVID-19 pneumonia.Quantitative imaging in medicine and surgery · 2022Article
- The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.Scientific reports · 2022Article
- Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia.La Radiologia medica · 2021Article
- Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.Scientific reports · 2021Article
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Authors and funding
17 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDifferentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT).
methodCOVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images.
resultsWe retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals.
conclusionsThe developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.
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