Clinical trial · Interventional
Intraoperative Ultrasound for Brain Tumor Surgery Enhanced by AI
Optimization of Intraoperative Ultrasound Use in Brain Tumor Surgery Through Artificial Intelligence-Based Techniques
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 18, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260918-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Brain Tumor Adult | Brain Neoplasm | ALIAS | 0.85 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| BrainUS-AI real-time intraoperative ultrasound segmentation system | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Real-time AI-assisted intraoperative ultrasound segmentation
- description
- 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/FPS, operational stability), feasibility/workflow impact, residual tumor detection agreement, and surgeon-reported usability will be prospectively collected across participating centers.
- interventionNames
- Device: BrainUS-AI real-time intraoperative ultrasound segmentation system
Primary outcomes (1)
- measure
- Diagnostic performance of BrainUS-AI for residual tumor detection at end of resection
- timeFrame
- During surgery (baseline, during resection, and end of resection), with the primary assessment at the end of resection on the final intraoperative ultrasound acquisition.
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion criteria: * 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. Exclusion criteria: • Inadequate ioUS image acquisition due to technical failure or intraoperative complications unrelated to the tumor.
References
Publications (2)
- RESULTCepeda S, Esteban-Sinovas O, Singh V, Shetty P, Moiyadi A, Dixon L, Weld A, Anichini G, Giannarou S, Camp S, Zemmoura I, Giammalva GR, Del Bene M, Barbotti A, DiMeco F, West TR, Nahed BV, Romero R, Arrese I, Hornero R, Sarabia R. Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS). Cancers (Basel). 2025 Jan 19;17(2):315. doi: 10.3390/cancers17020315. PMID 39858097
- RESULTCepeda S, Esteban-Sinovas O, Romero R, Singh V, Shett P, Moiyadi A, Zemmoura I, Giammalva GR, Del Bene M, Barbotti A, DiMeco F, West TR, Nahed BV, Arrese I, Hornero R, Sarabia R. Real-time brain tumor detection in intraoperative ultrasound: From model training to deployment in the operating room. Comput Biol Med. 2025 Jul;193:110481. doi: 10.1016/j.compbiomed.2025.110481. Epub 2025 May 30. PMID 40449046