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Automating checks of plan check automation.

ABSTRACT: While a few physicists have designed new plan check automation solutions for their clinics, fewer, if any, managed to adapt existing solutions. As complex and varied as the systems they check, these programs must gain the full confidence of those who would run them on countless patient plans. The present automation effort, planCheck, therefore focuses on versatility and ease of implementation and verification. To demonstrate this, we apply planCheck to proton gantry, stereotactic proton gantry, stereotactic proton fixed beam (STAR), and IMRT treatments.

SUBMITTER: Halabi T

PROVIDER: S-EPMC7678958 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Automating checks of plan check automation.

Halabi Tarek T Lu Hsiao-Ming HM

Journal of applied clinical medical physics 20140708 4

While a few physicists have designed new plan check automation solutions for their clinics, fewer, if any, managed to adapt existing solutions. As complex and varied as the systems they check, these programs must gain the full confidence of those who would run them on countless patient plans. The present automation effort, planCheck, therefore focuses on versatility and ease of implementation and verification. To demonstrate this, we apply planCheck to proton gantry, stereotactic proton gantry, ...[more]

PMID: 25207420

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Project description:PurposeThe purpose of this study was to develop an auto-planning platform to be interfaced with a commercial treatment planning system (TPS). The main goal was to obtain robust and high-quality plans for different anatomic sites and various dosimetric requirements.MethodsMonaco (Elekta, St. Louis, US) was the TPS in this work. All input parameters for inverse planning could be defined in a plan template inside Monaco. A software tool called Robot Framework was used to launch auto-planning trials with updated plan templates. The template modifier external to Monaco was the major component of our auto-planning platform. For current implementation, it was a rule-based system that mimics the trial-and-error process of an experienced planner. A template was automatically updated by changing the optimization constraints based on dosimetric evaluation of the plan obtained in the previous trial, along with the data of the iterative optimization extracted from Monaco. Treatment plans generated by Monaco with all plan evaluation criteria satisfied were considered acceptable, and such plans would be saved for further evaluation by clinicians. The auto-planning platform was validated for 10 prostate and 10 head-and-neck cases in comparison with clinical plans generated by experienced planners.ResultsThe performance and robustness of our auto-planning platform was tested with clinical cases of prostate and head and neck treatment. For prostate cases, automatically generated plans had very similar plan quality with the clinical plans, and the bladder volume receiving 62.5 Gy, 50 Gy, and 40 Gy in auto-plans was reduced by 1%, 3%, and 5%, respectively. For head and neck cases, auto-plans had better conformity with reduced dose to the normal structures but slightly higher dose inhomogeneity in the target volume. Remarkably, the maximum dose in the spinal cord and brain stem was reduced by more than 3.5 Gy in auto-plans. Fluence map optimization only with less than 30 trials was adequate to generate acceptable plans, and subsequent optimization for final plans was completed by Monaco without further intervention. The plan quality was weakly dependent on the parameter selection in the initial template and the choices of the step sizes for changing the constraint values.ConclusionAn automated planning platform to interface with Monaco was developed, and our reported tests showed preliminary results for prostate and head and neck cases.

Project description:Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system's recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.

Project description:ObjectivesWe aimed to estimate how many children were attending a universal preschool health screen and to identify characteristics associated with non-participation.DesignAnalysis of population-level linked administrative data.ParticipantsChildren were considered eligible for a B4 School Check for a given year if:(1) they were ever resident in New Zealand (NZ),(2) lived in NZ for at least 6 months during the reference year, (3) were alive at the end of the reference year, (4) either appeared in any hospital (including emergency) admissions, community pharmaceutical dispensing or general practitioner enrolment datasets during the reference year or (5) had a registered birth in NZ. We analysed 252 273 records over 4 years, from 1 July 2011 to 30 June 2015.ResultsWe found that participation rates varied for each component of the B4 School Check (in 2014/2015 91.8% for vision and hearing tests (VHTs), 87.2% for nurse checks (including height, weight, oral health, Strengths and Difficulties Questionnaire [SDQ] and parental evaluation of development status) and 62.1% for SDQ - Teacher [SDQ-T]), but participation rates for all components increased over time. Māori and Pacific children were less likely to complete the checks than non-Māori and non-Pacific children (for VHTs: Māori: OR=0.60[95% CI 0.61 to 0.58], Pacific: OR=0.58[95% CI 0.60 to 0.56], for nurse checks: Māori: OR=0.63[95% CI 0.64 to 0.61], Pacific: OR=0.67[95% CI 0.69 to0.65] and for SDQ-T: Māori: OR=0.76[95% CI 0.78 to 0.75], Pacific: OR=0.37[95% CI 0.38 to 0.36]). Children from socioeconomically deprived areas, with younger mothers, from rented homes, residing in larger households, with worse health status and with higher rates of residential mobility were less likely to participate in the B4 School Check than other children.ConclusionThe patterns of non-participation suggest a reinforcing of existing disparities, whereby the children most in need are not getting the services they potentially require. There needs to be an increased effort by public health organisations, community and whānau/family to ensure that all children are tested and screened.

Dataset Information

Automating checks of plan check automation.

Publications

Automating checks of plan check automation.

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