Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA, 98195, USA.
Med Phys. 2020 Jun;47(5):e168-e177. doi: 10.1002/mp.13445. Epub 2019 Mar 4.
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.
最近，机器学习在质量保证（QA）领域的应用呈爆炸式增长，产生了各种概念验证，其中许多都取得了有希望的成果。模型实施的预期结果包括改进规划时间、计划质量、先进剂量学 QA、预测性机器维护、增加安全检查以及开发新的 QA 范式的关键，这些范式由自适应规划驱动。在本文中，我们概述了几个研究领域，并讨论了每个领域所提出的一些独特挑战。
