Intraoperative Ultrasound for Brain Tumor Surgery Enhanced by AI
ConditionBrain Tumor Adult
Trial statusNot yet recruiting
Trial phasePhase 3
Trial typeInterventional
Biological sexAll
Age18+
SponsorHospital del Rio Hortega
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age ≥ 18 years.
Scheduled for craniotomy and resection of a brain tumor with ioUS planned as part of the standard surgical workflow.
Preoperative MRI available for surgical planning.
Ability to obtain informed consent from the patient or legal representative.
Disqualifiers
None
Trial design
Design model
Single group
Treatments tested in this trial
BrainUS-AI real-time intraoperative ultrasound segmentation system
DeviceA 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.
Treatment groups
100 Participants
are divided into 1 treatment groupGroup A: Real-time AI-assisted intraoperative ultrasound segmentationExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
Diagnostic performance of BrainUS-AI for residual tumor detection at end of resection
Residual tumor presence/absence will be determined by the BrainUS-AI segmentation overlay during the final intraoperative ultrasound acquisition (when the surgeon considers the resection complete). This binary classification (residual present/absent) will be compared against early postoperative MRI when available (reference standard), and agreement with the surgeon's intraoperative assessment will also be recorded. Diagnostic performance will be reported as sensitivity, specificity, PPV, and NPV with 95% confidence intervals, and concordance will be assessed using Cohen's kappa.
Time frame
During surgery (baseline, during resection, and end of resection), with the primary assessment at the end of resection on the final intraoperative ultrasound acquisition.Locations
Sponsors and contacts
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Hospital del Rio Hortega
Lead sponsor
University Hospital Bratislava
Collaborator
Source ClinicalTrials.gov
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