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BACKGROUND AND OBJECTIVE: Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. METHODS: The study is carried out on two groups of patients, including 138 patients with confirmed and 140 patients with suspected COVID-19. We focus on distinguishing pneumonia caused by COVID-19 from the suspected cases by segmentation of whole lung volume and extraction of 86 radiomics features. Followed by feature extraction, nine feature-selection procedures are used to identify valuable features. Then, ten ML classifiers are applied to classify and predict COVID-19 cases. Each ML models is trained and tested using a ten-fold cross-validation method. The predictive performance of each ML model is evaluated using the area under the curve (AUC) and accuracy. RESULTS: The range of accuracy and AUC is from 0.32 (recursive feature elimination [RFE]+Multinomial Naive Bayes [MNB] classifier) to 0.984 (RFE+bagging [BAG], RFE+decision tree [DT] classifiers) and 0.27 (mutual information [MI]+MNB classifier) to 0.997 (RFE+k-nearest neighborhood [KNN] classifier), respectively. There is no direct correlation among the number of the selected features, accuracy, and AUC, however, with changes in the number of the selected features, the accuracy and AUC values will change. Feature selection procedure RFE+BAG classifier and RFE+DT classifier achieve the highest prediction accuracy (accuracy: 0.984), followed by MI+Gaussian Naive Bayes (GNB) and logistic regression (LGR)+DT classifiers (accuracy: 0.976). RFE+KNN classifier as a feature selection procedure achieve the highest AUC (AUC: 0.997), followed by RFE+BAG classifier (AUC: 0.991) and RFE+gradient boosting decision tree (GBDT) classifier (AUC: 0.99). CONCLUSION: This study demonstrates that the ML model based on RFE+KNN classifier achieves the highest performance to differentiate patients with a confirmed infection caused by COVID-19 from the suspected cases.

Background This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. Results The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. Conclusions The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

Introduction With regards to the use of ionisation radiation in the computed tomography (CT), optimal parameters should be used to reduce the risk of incidence of secondary cancers in patients who are constantly exposed to X-rays. The aim of this study was to optimise the parameters used in CT scan of cervical vertebrae and neck soft tissue with minimal loss of image quality in emergency patients. Materials and methods In this study, the patients were divided into two groups. The first group consisted of patients scanned with default parameters and the second group scanned with optimised parameters. All the study has been implemented in emergency settings. The cases included cervical vertebrae and soft tissue protocols. Common CT dose descriptors including weighted computed tomography dose index (CTDIw), volumetric CTDI (CTDIvol), dose length product (DLP), effective dose (ED) and image noise were measured for each group. The ImpactDose program was used to estimate the organs doses. Statistical analysis was performed using Kruskal-Wallis test using SPSS software. Results There was no significant quality reduction in the optimised images. Decreasing in radiation dose parameters for the soft tissue was: kVp=16.7%, mAs=64.3% and pitch=24.1%, and for the cervical vertebrae was: kVp=16.7%, mAs=54.2% and pitch=48.3%. Consequently, decreasing these parameters reduced CTDIw=81.0%, CTDIvol=90.0% and DLP = 90.2% in the cervical vertebral protocol, as well as CTDIw=75.5%, CTDIvol=81.3% and DLP = 81.4% in the soft tissue protocol. Conclusion Regarding the results, the optimised parameters in the mentioned organ scan reduce the radiation dose in the target area and the organs surrounding. Therefore, these protocols can be used for reducing the risk of cancer.

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