Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms

Abstract

Objective: The aim of the study is to predict the absorbed radiation dose on thorax CT imaging in geriatric patients with COVID-19. Materials and Method: The SIEMENS SENSATION 64 CT scanner was performed with real protocols to patients (male/female phantom) using Monte Carlo simulation methods with the patient’s real height and weight nts and the actual parameters CT scanner. Absorbed organ doses have been calculated based on these Monte Carlo results. These results were used to predict the optimal absorbed radiation dose by Artificial Neural Network, Linear Discriminant Analysis, Random Forest Classification, and Naive-Bayes Classification algorithms. The dose values were clustered for genders by the Fuzzy C-Means algorithm. Results: The ages of the patients were between 60 and 70 years. The Body Mass Indexes of male and female patients were 26.11 ± 4.49 and 25.03 ± 4.86 kg/m2 respectively. All classification algorithms were validated with approximately 100% success. The Fuzzy C-Means technique was found to be successful in clustering the dose values for gender clusters. Conclusion: While the predicted and the observed values of patients do not change in the organs/tissues around and outside of the thorax, they generally vary in the intra-thoracic organs and tissues. It can be concluded that data-driven techniques are useful to obtain optimal radiation doses for organs/tissues in CT imaging.