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Intraoperative Ultrasound for Brain Tumor Surgery Enhanced by AI
Sponsor: Hospital del Rio Hortega
Summary
Intraoperative ultrasound is a versatile, low-cost imaging tool that has been shown to improve safety and efficacy in brain tumor surgery. However, its widespread adoption remains limited due to operator dependency, the complexity of image interpretation, the presence of artifacts, and a restricted field of view. This project aims to prospectively evaluate, in a multicenter and non-randomized setting, a prototype real-time deep learning-based segmentation model for brain tumor delineation in intraoperative ultrasound. The model is designed to facilitate the identification of tumor tissue during surgery, potentially enhancing intraoperative decision-making and surgical precision. By increasing the precision and accessibility of ioUS, this innovation is expected to enable safer and more complete resections, with the potential to improve both survival and quality of life for patients with brain tumors.
Official title: Optimization of Intraoperative Ultrasound Use in Brain Tumor Surgery Through Artificial Intelligence-Based Techniques
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
100
Start Date
2026-03
Completion Date
2028-06
Last Updated
2026-01-29
Healthy Volunteers
No
Conditions
Interventions
BrainUS-AI real-time intraoperative ultrasound segmentation system
A prototype AI-based device (software system) that performs real-time deep learning segmentation of brain tumor tissue on intraoperative ultrasound (ioUS) and displays the segmentation as an overlay on the live ultrasound feed during surgery. The system is used as an adjunct to standard-of-care ioUS without mandating any change to the planned surgical strategy; intraoperative decisions remain under the surgeon's responsibility. System logs capture processing performance (e.g., FPS, end-to-end latency, operational uptime) and outputs used for subsequent technical validation and workflow/usability assessments.