AI Article Synopsis

  • A standardized fully automatic workflow for deep learning-based auto-segmentation in radiotherapy was developed and compared to manual methods for safety and efficiency.
  • Safety evaluations identified and reduced eight failure modes, with several having significant potential impacts on patient safety.
  • The automatic workflow improved efficiency by eliminating mouse clicks, highlighting advancements in both safety and workflow efficiency.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11381787PMC
http://dx.doi.org/10.1016/j.phro.2024.100627DOI Listing

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