Hamd, Zuhal Y. and Alrebdi, H.I. and Osman, Eyas G. and Awwad, Areej and Alnawwaf, Layan and Nashri, Nawal and Alfnekh, Rema and Khandaker, Mayeen Uddin * (2023) Optimization of chest X-ray exposure factors using machine learning algorithm. Journal of Radiation Research and Applied Sciences, 16 (1). ISSN 1687-8507
Full text not available from this repository. (Request a copy)Abstract
A better quality radiographic image helps the radiologist to make a proper diagnosis of the disease. In general, the use of more radiation provides a better quality image, but it gives the patient a higher radiation dose, which shows the need for optimization of imaging conditions to minimize the risk to patients from excessive radiation exposure. In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. Patient data were collected from the King Abdullah bin Abdulaziz University Hospital, Saudi Arabia. The role of BMI in the selection of radiation exposure factors (kVp, mAs) was evaluated. It has been found that the BMI of each patient has specific exposure factors, and that if it gets higher than the specific value it could harm the patient’s health. The obtained results provide detailed information about the relation between BMI and optimal chest X-ray exposure factors without affecting the quality of the X-ray image.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | exposure factors; body mass index; chest x-ray; machine learning |
| Subjects: | R Medicine > RC Internal medicine T Technology > TR Photography |
| Divisions: | Others > Non Sunway Academics Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Research Centre for Applied Physics and Radiation Technologies |
| Depositing User: | Ms Yong Yee Chan |
| Related URLs: | |
| Date Deposited: | 15 Jun 2023 04:01 |
| Last Modified: | 15 Jun 2023 04:01 |
| URI: | http://eprints.sunway.edu.my/id/eprint/2241 |
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