Improving STELLA Linac design by early fault detection and preventative maintenance for reducing Linac downtime using AI and ML

Globally, cancer is the second most prevalent cause of death among non-communicable diseases and it is estimated that the annual global cancer incidence will rise from 19.3 million cases and 10 million deaths in 2020 to as many as 27.5 million cases and 16.3 deaths in 2040. About 65 to 70% of this increase will occur in Low- and Middle-Income Countries (LMICs).

Radiation therapy (RT) is critical for the treatment or palliation of over half of all patients with cancer, yet there is a global shortage in access to this treatment. Linear accelerators (LINACs) offer state-of-the-art treatment but this technology is high cost to acquire, operate and service, especially for Low- and Middle-Income Countries (LMICs), and often their harsh environment negatively affects machine performance causing large downtimes.

RT equipment in LMICs is subject to more downtime and higher failure rates compared to HIC (Higher Income Countries) partly due to environmental factors, but also a lack of preventative maintenance and early recognition of problems.

This project idea builds on prior studies under ITAR (Innovative Technologies towards building Affordable and equitable global Radiotherapy capacity) where the faults and breakdowns in Linacs were examined. Here how AI and ML could be used for early detection, intervention, and prevention to reduce the long downtimes that occur especially in developing countries will be examined.

The current project will look at the use AI/ML tools to detect faults in selected major components of Linacs that breakdown most often and using the data, develop automated program(s) that can eventually predict faults remotely to alert users to needed repairs in time to order spare parts and reduce downtime of the facility.