Using a Novel Change Detection Algorithm to Predict Daily Linear Accelerator Output Changes in a Radiation Department.

Abstract Machine learning tools and techniques were utilised to create a Novel Change Detection Algorithm (NCDA) as a supplementary quality assurance tool to alert users to potential significant changes in linear accelerator output using daily measurements. A prototype model was developed and validated that provides a forecast for the daily dose and indicated when there is the potential for the output to change beyond what is considered normal daily drift. The model provided a good fit with the validation dataset used, meaning that trends in daily output were easy to identify.The NCDA can be used either daily whereby output data is manually entered from the QA3 user interface, or on a weekly basis allowing input of the other data which is not traditionally presented on the interface. The NCDA should serve as a tool to alert users when data is trending out of tolerance and inform of possible output adjustments, therefore enabling effective allocation of time and resources.