Fully automated detection and identification of CSF shunt valves using YOLOv8 and a class-based reference image assignment as a safety mechanism.

ABSTRACT: The study aimed to develop and evaluate an algorithm based on the YOLOv8x framework to automatically detect and identify cerebrospinal fluid (CSF) shunt valves. This approach seeks to streamline the diagnostic process identifying shunt valve types and pressure levels. A retrospective cohort of 2701 anonymized radiographs comprising six types of CSF shunt valves was used. Data augmentation techniques such as flipping, scaling, and mosaic augmentation were applied during training to enhance robustness. The dataset was split into 80% training and 20% testing subsets as part of a 5-fold cross-validation. Validation was conducted on a separate test set of 295 images using metrics such as mean Average Precision (mAP) at intersection over union thresholds of 50% (mAP50) as well as precision, reca

SUBMITTER: Holtkamp M 

PROVIDER: S-EPMC12639084 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature