Prospective risk analysis of the online-adaptive artificial intelligence-driven workflow using the Ethos treatment system

Wegener, Sonja; Exner, Florian; Weick, Stefan; Stark, Silke; Hutzel, Heike; Lutyj, Paul; Tamihardja, Jörg; Razinskas, Gary

doi:10.1016/j.zemedi.2022.11.004

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…This system adjusts radiotherapy treatment plans daily according to the anatomical changes. The system creates an adapted plan using artificial intelligence, thus speeding up the workflow [36]. The new feature IOE and its innovative workflow in generating the reference plan is designed to streamline the treatment planning process by automating the insertion of optimization parameters based on the physician's planning directives.…”

Section: Artificial Intelligence In Image Guided Radiation Therapymentioning

confidence: 99%

Unravelling the landscape of image-guided radiotherapy: a comprehensive overview

Sarma,

Kashya,

Medhi

et al. 2024

Palliat Med Pract

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Section: Artificial Intelligence In Image Guided Radiation Therapymentioning

confidence: 99%

Unravelling the landscape of image-guided radiotherapy: a comprehensive overview

Sarma,

Kashya,

Medhi

et al. 2024

Palliat Med Pract

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“…Despite a few published articles on commissioning and dosimetric studies of the CBCT-based OART platform, 9 , 10 , 11 , 12 , 13 a pressing need remains for published guidance on the comprehensive treatment workflow and individual responsibilities. In this study, we conducted a failure mode and effects analysis (FMEA) to identify high-risk failure modes and developed logistically feasible clinical workflows for OART, which are categorized into 4 components: 1 site-specific templates preparation, 2 pretreatment planning and verification, 3 on-treatment procedures, and 4 posttreatment verification, along with safety checklists.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow

Wang,

Price,

Elliston

et al. 2024

Advances in Radiation Oncology

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Improving the safety of radiotherapy treatment processes via incident‐driven FMEA feedback loops

Kornek,

Lotter,

Szkitsak

et al. 2024

J Applied Clin Med Phys

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BackgroundFailure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates.PurposeA novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes.MethodsTwenty‐three pre‐identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal–Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts’ occurrence ratings by means of the number of incoming reports.ResultsOver a 15‐month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014–0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003–0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p = 0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved.ConclusionsOur approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

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Prospective risk analysis of the online-adaptive artificial intelligence-driven workflow using the Ethos treatment system

Cited by 9 publications

References 16 publications

Unravelling the landscape of image-guided radiotherapy: a comprehensive overview

Unravelling the landscape of image-guided radiotherapy: a comprehensive overview

Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow

Improving the safety of radiotherapy treatment processes via incident‐driven FMEA feedback loops

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