Date Approved
8-10-2021
Graduate Degree Type
Project
Degree Name
Medical Dosimetry (M.S.)
Degree Program
Health Professions
First Advisor
Heather Wallace
Academic Year
2020/2021
Abstract
Abstract
Introduction:
Artificial intelligence (AI) is the process of automating tasks that were traditionally done manually. AI can be used throughout the dosimetry treatment planning process for external beam radiation treatments. Artificial intelligence has the opportunity to help reduce treatment planning time and help to increase the quality of treatment plans. The workload of cancer treatment facilities across the United States varies greatly. This study aims to determine if the workload of a treatment facility has an impact upon the usage of artificial intelligence during treatment planning.
Methods:
This study is a qualitative study of 96 survey participants. The survey was created using the Qualtrics program and was distributed through the American Association of Medical Dosimetrists (AAMD) to all of their members. The survey was open for responses for 22 days. The survey consisted of 19 multiple choice, and interval questions. The results of the survey were interpreted using 2-way ANOVA testing and frequency data analysis.
Results:
The results of this study indicated that there is no statistical significance between the average number of patients being simulated weekly at a facility and the usage of artificial intelligence in external, beam treatment planning (P=0.4135, P=0.13, P=0.31, P=0.36, P=0.18, P=0.97). The results also indicated there was no statistical significance between the average number of external beam treatment plans created weekly and the usage of artificial intelligence in treatment planning (P=0.17. P=0.64, P=0.83, P=0.97, P=0.63).
Conclusion:
There was no correlation between the average weekly number of simulated patients or the average number of weekly external beam plans created and the usage of artificial intelligence in treatment planning. Another study with a larger sample size would be beneficial to further examine any possible correlations.
ScholarWorks Citation
Ervick, Casey Joy, "A Quantitative Study of Correlations between Patient Load with the Usage of Artificial Intelligence Treatment Planning tools (auto planning, adaptive RT, Clear Check, Deformable Dose, Auto-contouring, EZFluence, etc.) A United States Context" (2021). Culminating Experience Projects. 57.
https://scholarworks.gvsu.edu/gradprojects/57
