Evidence map›Paper›PMID 41674916›Full record

ArticleJournal of vascular surgery cases and innovative techniques2026

Integration of high-resolution mixed reality models into resident training.

Chaitanya Dandu, Brandon Creisher, Melissa Davis, MingDe Lin, Stefan Schenke, Edouard Aboian

Abstract read
In one paragraph

Article in Journal of vascular surgery cases and innovative techniques, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chaitanya DanduDepartment of Vascular Surgery and Endovascular Therapy, Yale School of Medicine, New Haven, CT.
Brandon CreisherDepartment of Vascular Surgery and Endovascular Therapy, Yale School of Medicine, New Haven, CT.
Melissa DavisDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
MingDe LinVisage Imaging, Inc, San Diego, CA.
Stefan SchenkeVisage Imaging, GmbH, Berlin, Germany.
Edouard AboianDepartment of Vascular Surgery and Endovascular Therapy, Yale School of Medicine, New Haven, CT.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Historically, surgical anatomy presented visualization and spatial awareness challenges for surgical trainees. The development of spatial computing provided a new avenue for anatomy and image visualization. We present a case of novel application of mixed reality headset in the emergent vascular care. Methods: A retrospective analysis of the emergent endovascular repair of an aortoenteric fistula was performed using an Apple Vision Pro headset and Visage Ease application. A three-dimensional (3D) rendering of chest computed tomography scan using Visage Ease was performed. Ease of use, anatomical visualization, and spatial awareness were evaluated. Results: A 3D model demonstrated the location of stent erosion into the aorta. Rotating, the model in 3D space using gesture-based controls was intuitive, responsive, and easy. The operator learning curve was minimal. Spatial awareness appeared superior to standard picture archiving and communication system workstation-based anatomical visualization. Conclusions: Mixed reality based on spatial computing provides a new avenue for student and resident training in anatomy and radiology. It allows for superb visualization and better spatial awareness than when viewed on a workstation platform and ultimately may provide better case execution.

Indexed as

EducationSpatial computingSurgical anatomyVascular surgery

Identifiers

PMID41674916
PMCPMC12887252

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.