| Autor: |
Hasan Cavus, Philippe Bulens, Koen Tournel, Marc Orlandini, Alexandra Jankelevitch, Wouter Crijns, Brigitte Reniers |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Physics and Imaging in Radiation Oncology, Vol 31, Iss , Pp 100627- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-6316 |
| DOI: |
10.1016/j.phro.2024.100627 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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