Evidence map›Paper›PMID 39735694›Full record

ArticleVirtual reality2025

PreVISE: an efficient virtual reality system for SEEG surgical planning.

Pascal Spiegler, Haitham Abdelsalam, Owen Hellum, Aristides Hadjinicolaou, Alexander G Weil, Yiming Xiao

Abstract read
In one paragraph

Article in Virtual reality, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pascal Spiegler *Department of Computer Science and Software Engineering, Concordia University, Montreal, Québec Canada.
Haitham Abdelsalam *Department of Computer Science and Software Engineering, Concordia University, Montreal, Québec Canada.
Owen HellumDepartment of Computer Science and Software Engineering, Concordia University, Montreal, Québec Canada.
Aristides HadjinicolaouDepartment of Pediatrics, Division of Neurology, Sainte-Justine University Hospital Center, Montreal, Québec Canada.
Alexander G WeilDepartment of Surgery, Division of Neurosurgery, Sainte-Justine University Hospital Center, Montreal, Québec Canada.
Yiming XiaoDepartment of Computer Science and Software Engineering, Concordia University, Montreal, Québec Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy is a neurological disorder characterized by recurring seizures that can cause a wide range of symptoms. Stereo-electroencephalography (SEEG) is a diagnostic procedure where multiple electrodes are stereotactically implanted within predefined brain regions to identify the seizure onset zone, which needs to be surgically removed or disconnected to achieve remission of focal epilepsy. This procedure is complex and challenging due to two main reasons. First, as electrode placement requires good accuracy in desired brain regions, excellent knowledge and understanding of the 3D brain anatomy is required. Second, as typically multiple SEEG electrodes need to be implanted, the positioning of intracerebral electrodes must avoid critical structures (e.g., blood vessels) to ensure patient safety. Traditional SEEG surgical planning relies on 2D display of multi-contrast volumetric medical imaging data, and places a high cognitive demand for surgeons' spatial understanding, resulting in potentially sub-optimal surgical plans and extensive planning time (~ 15 min per electrode). In contrast, virtual reality (VR) presents an intuitive and immersive approach that can offer more intuitive visualization of 3D data as well as potentially enhanced efficiency for neurosurgical planning. Unfortunately, existing VR systems for SEEG surgery only focus on the visualization of post-surgical scans to confirm electrode placement. To address the need, we introduce the first VR system for SEEG planning that integrates user-friendly and efficient visualization and interaction strategies while providing real-time feedback metrics, including distances to nearest blood vessels, angles of insertion, and the overall surgical quality scores. The system reduces the surgical planning time by 91%.

Indexed as

Image-guided-surgeryNeurosurgical planningStereoelectroencephalographyVirtual reality

Identifiers

PMID39735694
PMCPMC11669611

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.