Radiation Oncology is the medical care and management of patients with cancer primarily through application of targeted irradiation of the cancerous volume, whilst minimising and sparing dose to surrounding healthy tissue.
Daily measurements are taken by a team of Medical Physicists to assess machine performance and to primarily monitor the machines output. If these measurements are out of tolerance then further action is required to investigate and correct this, resulting in machine downtime and delayed treatment for patients, which can add stress during an already stressful time.
The Radiation Physicists require a system that automatically monitors these daily outputs and identifies potential deviation from normal performance, allowing for proper resource allocation and reduction in machine downtime and delays.
This project aims at investigating the use of machine learning, specifically change detection, to analyse and predict the performance of machines used for radiation treatment The outcome of this project will directly aid in helping the physicists provide a better healthcare to patients in New Zealand.
Evaluation with experimental results will be conducted to measure the effectiveness and quality of the proposed solution.
Based on the proposed solution, develop a software system that realises the software generation environment with evaluations.
Lab allocations have not been finalised