Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning.

Journal: Medical physics
Published Date:

Abstract

The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a variety of proofs-of-concept many with promising results. Expected outcomes of model implementation include improvements in planning time, plan quality, advanced dosimetric QA, predictive machine maintenance, increased safety checks, and developments key for new QA paradigms driven by adaptive planning. In this article, we outline several areas of research and discuss some of the unique challenges each area presents.

Authors

  • Alan M Kalet
    Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA, 98195, USA.
  • Samuel M H Luk
    Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA, 98195, USA.
  • Mark H Phillips
    Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA, 98195, USA.