Statistical process control for performance monitoring and continuous quality assurance of deep learning segmentations in radiotherapy.

ABSTRACT:

Background and purpose

After clinical implementation of deep learning segmentation (DLS) models it is highly recommended to perform routine quality assurance (QA). Through forthcoming regulations of the EU AI Act state that the output of any DLS model needs to be logged, enabling continuous QA (CQA) to monitor DLS performance and introduce alarms. Therefore, the goal was to implement a CQA framework for DLS in radiotherapy.

Materials and methods

The direct output of the DLS models and the clinically approved delineations (CS) were automatically exported, after which geometric metrics were calculated. For each combination between a region of interest (ROI) and metric, a target, lower and upper control limit were determined, based on statistical process control (SPC). Adapted

SUBMITTER: van Acht N 

PROVIDER: S-EPMC12702409 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature