Intraoperative Ultrasound for Brain Tumor Surgery Enhanced by AI (BrainUS-AI)

January 22, 2026 updated by: Santiago Cepeda, Hospital del Rio Hortega

Optimization of Intraoperative Ultrasound Use in Brain Tumor Surgery Through Artificial Intelligence-Based Techniques

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Brain tumor surgery presents major challenges due to the complex anatomy of the brain and the infiltrative nature of these lesions, which are often located near eloquent areas. One of the key determinants of patient survival is the extent of tumor resection, provided it can be achieved without compromising neurological function 1. To maximize safe resection, neurosurgeons rely on a variety of intraoperative adjuncts, including fluorescent agents, neuronavigation, direct electrical stimulation, and advanced intraoperative imaging techniques, most notably intraoperative magnetic resonance imaging (ioMRI) and intraoperative ultrasound (ioUS) 2.

Although ioMRI offers excellent resolution and accuracy, its high cost, logistical demands, and complexity of integration limit its availability to a small number of specialized centers 3. In contrast, ioUS is a low-cost, versatile modality that integrates naturally into the surgical workflow 4-6. Nevertheless, its broader adoption has been limited by several factors: high operator dependency, a steep learning curve, and interpretation challenges related to artifacts, non-standard imaging planes, low contrast between tumor and normal brain, and a restricted field of view.

Over the past decades, research on AI-based segmentation of brain tumors has advanced substantially, but most work has focused on MRI 7. In the context of ioUS, early studies such as Ritschel et al. 8 demonstrated that supervised classification models (e.g., support vector machines) could distinguish tumor from healthy tissue in contrast-enhanced ultrasound, but these approaches were labor-intensive and limited to small datasets. Ilunga-Mbuyamba et al. 9 later investigated multimodal registration between ioUS and MRI to enhance segmentation, but clinical feasibility was constrained by the need for accurate co-registration. More recently, deep learning-based approaches by Canalini et al. 10 and Carton et al. 11 have been applied to segment surgical cavities and tumor volumes in ioUS images.

State-of-the-art methods such as those reported by Faanes et al. 12, using nnU-Net architectures, have achieved promising Dice similarity coefficients of 0.6-0.9 on public datasets such as RESECT-SEG 13 and ReMIND 14. However, these models were not designed for real-time inference and have not undergone validation in live surgical settings. Other approaches, such as that of Dorent et al. 15, have relied on synthetic ultrasound images derived from preoperative MRI, raising concerns about generalizability to real ioUS data. Overall, despite these advances, clinical translation remains limited due to the unique challenges of ioUS, including lower spatial resolution, image heterogeneity, and variability in acquisition protocols.

In other medical domains, AI-assisted ultrasound segmentation has demonstrated real-time feasibility. For example, Hu et al. 16 implemented U-Net-based models for breast lesion segmentation at 16 frames per second (FPS) with Dice scores exceeding 0.75, while Wei et al. 17 applied YOLO-based detection to identify carotid plaques with 98.5% accuracy at 39 FPS. Despite their efficiency and accuracy, similar approaches have yet to be implemented and clinically validated for brain tumor surgery using ioUS.

Our project aims to address this gap by conducting a multicenter, prospective, non-randomized validation of a prototype deep learning-based segmentation model specifically designed for real-time intraoperative brain tumor ultrasound. The model operates at surgical frame rates, automatically delineating tumor boundaries directly on the live ultrasound feed, with the goal of assisting intraoperative decision-making, maximizing the extent of resection when oncologically appropriate, and preserving neurological function.

This study builds upon our prior work, which has established a strong scientific foundation for the proposed validation. In our recent publication, "Deep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS)" 18, we trained and validated a deep learning model for brain tumor segmentation using multicenter data from BraTioUS-DB-marking a milestone in ioUS research. In a second study, "Real-Time Brain Tumor Detection in Intraoperative Ultrasound: From Model Training to Deployment in the Operating Room"19, we developed and prospectively evaluated a real-time computer vision detection model in the operating room. Together, these contributions provide a robust framework for advancing real-time AI-based segmentation in intraoperative ultrasound, directly aligned with the objectives of the present study.

Study Type

Interventional

Enrollment (Estimated)

100

Phase

  • Phase 3

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Real-time AI-assisted intraoperative ultrasound segmentation
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.
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/usability assessments.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance of BrainUS-AI for residual tumor detection at end of resection
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.
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.
During surgery (baseline, during resection, and end of resection), with the primary assessment at the end of resection on the final intraoperative ultrasound acquisition.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Hospital del Rio Hortega

Collaborators

Department of Neurosurgery, Bratislava University Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 1, 2026

Primary Completion (Estimated)

December 1, 2027

Study Completion (Estimated)

June 1, 2028

Study Registration Dates

First Submitted

January 22, 2026

First Submitted That Met QC Criteria

January 22, 2026

First Posted (Actual)

January 29, 2026

Study Record Updates

Last Update Posted (Actual)

January 29, 2026

Last Update Submitted That Met QC Criteria

January 22, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • PI-25-191-H

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Brain Tumor Adult

Clinical Trials on BrainUS-AI real-time intraoperative ultrasound segmentation system

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Mauritania

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe