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Official websites use. Share sensitive information only on official, secure websites. Correspondence: scepedac saludcastillayleon. This study explores the use of artificial intelligence to improve the accuracy of intraoperative ultrasound ioUS imaging for glioma segmentation during neurosurgery. By training a deep learning model on data from multiple centers, this research demonstrates the potential for automated tumor delineation, despite challenges such as image noise and variability.
The model was tested on independent datasets and showed strong performance overall, although external validation highlighted areas for improvement. Notably, the model demonstrated generalizability across ioUS systems from different scanner types and manufacturers, underscoring its robustness in diverse clinical settings.
These findings emphasize the feasibility of using AI to enhance ioUS imaging, paving the way for more precise and efficient tumor resections in clinical practice. Background: Intraoperative ultrasound ioUS provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness.
Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability.