Department of Computer Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
Department of Mathematics Statistics and Computer, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand.
J Appl Clin Med Phys. 2020 Aug;21(8):73-82. doi: 10.1002/acm2.12917. Epub 2020 Jun 15.
A predictive linac quality assurance system based on the output of the Machine Performance Check (MPC) application was developed using statistical process control and autoregressive integrated moving average forecast modeling. The aim of this study is to demonstrate the feasibility of predictive quality assurance based on MPC tests that allow proactive preventative maintenance procedures to be carried out to better ensure optimal linac performance and minimize downtime.
Daily MPC data were acquired for a total of 490 measurements. The initial 85% of data were used in prediction model learning with the autoregressive integrated moving average technique and in calculating upper and lower control limits for statistical process control analysis. The remaining 15% of data were used in testing the accuracy of the predictions of the proposed system. Two types of prediction were studied, namely, one-step-ahead values for predicting the next day's quality assurance results and six-step-ahead values for predicting up to a week ahead. Results that fall within the upper and lower control limits indicate a normal stage of machine performance, while the tolerance, determined from AAPM TG-142, is the clinically required performance. The gap between the control limits and the clinical tolerances (as the warning stage) provides a window of opportunity for rectifying linac performance issues before they become clinically significant. The accuracy of the predictive model was tested using the root-mean-square error, absolute error, and average accuracy rate for all MPC test parameters.
The accuracy of the predictive model is considered high (average root-mean-square error and absolute error for all parameters of less than 0.05). The average accuracy rate for indicating the normal/warning stages was higher than 85.00%.
Predictive quality assurance with the MPC will allow preventative maintenance, which could lead to improved linac performance and a reduction in unscheduled linac downtime.
本研究旨在演示基于 MPC 测试的预测性质量保证的可行性，通过该测试可以进行主动预防性维护，从而更好地确保直线加速器的性能优化和减少计划外停机时间。我们开发了一种基于统计过程控制和自回归综合移动平均预测模型的、基于输出的预测性直线加速器质量保证系统。方法和材料：共采集了 490 次 MPC 测试的每日数据。最初 85%的数据用于预测模型学习，采用自回归综合移动平均技术，并计算统计过程控制分析的上下控制限。其余 15%的数据用于测试所提出系统预测准确性。研究了两种预测类型，一种是用于预测次日质量保证结果的一步预测值，另一种是用于预测一周内的六步预测值。落在上下控制限内的结果表明机器性能处于正常阶段，而 AAPM TG-142 确定的容差是临床所需的性能。控制限与临床容差之间的差距（作为警告阶段）为在直线加速器性能问题变得具有临床意义之前进行纠正提供了机会。使用均方根误差、绝对误差和所有 MPC 测试参数的平均准确率来测试预测模型的准确性。结果：预测模型的准确性被认为较高（所有参数的平均均方根误差和绝对误差均小于 0.05）。指示正常/警告阶段的平均准确率高于 85.00%。结论：MPC 的预测性质量保证将允许进行预防性维护，从而提高直线加速器的性能并减少计划外停机时间。
