[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100621875":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":21,"locations":20,"responsibleParty":27,"collaborators":31,"id":34,"slug":35,"hasResults":36,"nctId":37,"briefTitle":38,"officialTitle":39,"acronym":40,"eligibilityCriteria":41,"healthyVolunteers":36,"sex":42,"minAge":43,"maxAge":20,"enrollmentInfo":44,"targetDuration":20,"studyType":47,"phases":48,"briefSummary":50,"conditions":51,"keywords":53,"overallStatus":63,"whyStopped":20,"lastUpdateSubmitDate":64,"lastUpdatePostDateStruct":65,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":20},{"fullName":5,"class":6},"Hospital del Rio Hortega","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Real-time AI-assisted intraoperative ultrasound segmentation","EXPERIMENTAL","Participants undergoing standard-of-care brain tumor resection with intraoperative ultrasound (ioUS) will use a prototype real-time deep learning-based segmentation system that overlays automated tumor delineation on the live ultrasound feed during surgery. The tool is used as an adjunct to routine intraoperative imaging and does not mandate changes to the surgical strategy; the surgeon remains fully responsible for intraoperative decision-making. Technical performance (e.g., segmentation accuracy, latency\u002FFPS, operational stability), feasibility\u002Fworkflow impact, residual tumor detection agreement, and surgeon-reported usability will be prospectively collected across participating centers.",[13],"Device: BrainUS-AI real-time intraoperative ultrasound segmentation system",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":20},"DEVICE","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\u002Fusability assessments.",[9],null,[22],{"name":23,"role":24,"phone":25,"phoneExt":20,"email":26},"Santiago Cepeda, MD., Ph.D.","CONTACT","+34651035158","scepedac@saludcastillayleon.es",{"type":28,"investigatorFullName":29,"investigatorTitle":30,"investigatorAffiliation":5,"oldNameTitle":20,"oldOrganization":20},"PRINCIPAL_INVESTIGATOR","Santiago Cepeda","Staff Neurosurgeon",[32],{"name":33,"class":6},"University Hospital Bratislava","100621875","phase-3-intraoperative-ultrasound-for-brain-tumor-surgery-enhanced-by-ai-100621875",false,"NCT07376304","Intraoperative Ultrasound for Brain Tumor Surgery Enhanced by AI","Optimization of Intraoperative Ultrasound Use in Brain Tumor Surgery Through Artificial Intelligence-Based Techniques","BrainUS-AI","Inclusion criteria:\n\n* Age ≥ 18 years.\n* Scheduled for craniotomy and resection of a brain tumor with ioUS planned as part of the standard surgical workflow.\n* Preoperative MRI available for surgical planning.\n* Ability to obtain informed consent from the patient or legal representative.\n\nExclusion criteria:\n\n• Inadequate ioUS image acquisition due to technical failure or intraoperative complications unrelated to the tumor.","ALL","18 Years",{"count":45,"type":46},100,"ESTIMATED","INTERVENTIONAL",[49],"PHASE3","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.\n\nThis 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.\n\nBy 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.",[52],"Brain Tumor Adult",[54,55,56,57,58,59,60,61,62],"brain tumor","glioma","glioblastoma","low-grade glioma","high-grade glioma","ious","computer vision","AI","ultrasound","NOT_YET_RECRUITING","2026-01-22",{"date":66,"type":67},"2026-01-29","ACTUAL",{"date":69,"type":46},"2026-03",{"date":71,"type":46},"2028-06",{"name":5,"class":6}]