Evidence map›Paper›PMID 42270923›Full record

ArticleCommunications engineering2026

Real-time 3D ultrasound in augmented reality accelerates training and narrows novice-expert performance gaps.

Jason F Hou, Shrihari Viswanath, Cinay Dilibal, Bowen Wu, Tanisha Shende, Canan Dagdeviren

Abstract read
In one paragraph

Article in Communications engineering, 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.

Jason F HouMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-9131-9583
Shrihari ViswanathMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Cinay DilibalMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bowen WuDepartment of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Tanisha ShendeMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Canan DagdevirenMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. canand@media.mit.edu.ORCID http://orcid.org/0000-0002-2032-792X

Funding

National Science Foundation (NSF) 2044688
6 · The paper itself

Abstract

Ultrasound imaging requires users to infer three-dimensional anatomy from two-dimensional slices, imposing steep training demands that limit broader adoption. Here we present AR-VIU, a mixed-reality platform that streams real-time volumetric ultrasound as point-cloud renderings into an augmented-reality headset with true-scale spatial registration. To isolate the contributions of volumetric imaging and immersive display, we tested four conditions-two-dimensional imaging on a screen, two-dimensional imaging in augmented reality, three-dimensional imaging on a screen, and three-dimensional imaging in augmented reality-in a controlled study with 18 participants (9 novices, 9 experts). Participants performed object recognition and localization tasks. The augmented-reality volumetric system was associated with the highest accuracy, lowest variability, and near-elimination of the novice-expert performance gap. These results demonstrate technical feasibility for real-time three-dimensional ultrasound in mixed reality and establish an evaluation framework for perceptual and cognitive performance in clinically relevant scenarios, with near-term applications in training and education.

Identifiers

PMID42270923
PMCPMC13254270

What OpenQuestion holds

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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.