Evidence map›Paper›PMID 42250192›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Markerless augmented reality registration for surgical guidance: a multi-anatomy clinical accuracy study.

Yue Yang, Fabian Necker, Christoph Leuze, Michelle Chen, Andrey Finegersh, Jake Lee, Vasu Divi, Bruce Daniel, Brian Hargreaves, Jie Ying Wu and 1 more

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

11 authors.

Yue YangSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Fabian NeckerSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Christoph LeuzeSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Michelle ChenSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Andrey FinegershSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Jake LeeSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Vasu DiviSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Bruce DanielSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Brian HargreavesSchool of Medicine, Stanford University, Stanford, CA, 94305, USA.
Jie Ying WuVanderbilt Institute of Surgery and Engineering, Nashville, TN, 37232, USA.
Fred M BaikSchool of Medicine, Stanford University, Stanford, CA, 94305, USA. fbaik@stanford.edu.ORCID http://orcid.org/0000-0001-8949-4577

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn this paper, we develop and clinically evaluate a depth-only, markerless augmented reality (AR) registration pipeline on a head-mounted display, and assess accuracy across small, or low-curvature anatomies in real-life operative settings.

methodsIn HoloLens 2, we align Articulated HAnd Tracking (AHAT) depth to Computed Tomography (CT)-derived skin meshes via (i) depth-bias correction, (ii) brief human-in-the-loop initialization, (iii) global and local registration. We validated the surface-tracing error metric by comparing "skin-to-bone" relative distances to CT ground truth on leg and foot models, using an AR tracked tool. We then performed seven intraoperative target trials (feet

resultsPreclinical validation showed tight agreement between AR-traced and CT distances (leg: median

Indexed as

Augmented RealityImaging, Three-DimensionalSurgery, Computer-AssistedTomography, X-Ray ComputedHumansFiducial free patient registrationMarkerless augmented realityMarkerless registrationMedical ARSurgical ARSurgical navigation

Identifiers

PMID42250192

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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.