Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology Pub Date : 2024-07-01 DOI:10.1016/j.phro.2024.100627

Hasan Cavus , Philippe Bulens , Koen Tournel , Marc Orlandini , Alexandra Jankelevitch , Wouter Crijns , Brigitte Reniers

{"title":"Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications","authors":"Hasan Cavus , Philippe Bulens , Koen Tournel , Marc Orlandini , Alexandra Jankelevitch , Wouter Crijns , Brigitte Reniers","doi":"10.1016/j.phro.2024.100627","DOIUrl":null,"url":null,"abstract":"<div><p>Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value >125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000976/pdfft?md5=2b3abdc79a31bbca036b2178ac496af9&pid=1-s2.0-S2405631624000976-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics and Imaging in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405631624000976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value >125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow.

查看原文本刊更多论文

使用三种基于深度学习的商用应用程序，实现放射治疗自动分割全自动工作流程的安全性和效率

放疗自动分割技术的发展需要可靠高效的工作流程。因此，我们为三种市面上基于深度学习的自动分割应用开发了标准化的全自动工作流程，并与手动工作流程进行了安全和效率方面的比较。通过故障模式和影响分析，对工作流程进行了安全性评估。值得注意的是，减少了八种失效模式，其中七种的严重性系数≥7，表明对患者的影响，两种的风险优先级数值为125，评估相对风险水平。以鼠标点击次数衡量的效率显示，自动工作流程的点击次数为零。这种自动化说明工作流程的安全性和效率都得到了提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation

CiteScore

5.30

自引率

18.90%

发文量

审稿时长

6 weeks