Evidence map›Paper›PMID 37432760›Full record

ArticleJournal of anatomy2023

A workflow for the creation of photorealistic 3D cadaveric models using photogrammetry.

Morgan Titmus, Gary Whittaker, Milo Radunski, Paul Ellery, Beatriz Ir de Oliveira, Hannah Radley, Petra Helmholz, Zhonghua Sun

Abstract read
In one paragraph

Article in Journal of anatomy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Immersive Neurosurgical Anatomy Using Photogrammetry: Technical Note and Scoping Review.Journal of neurological surgery. Part B, Skull base · 2026
    Article
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  5. Article
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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

8 authors.

Morgan TitmusCurtin Medical School, Curtin University, Perth, Australia.ORCID 0000-0001-9553-6841
Gary WhittakerCurtin Medical School, Curtin University, Perth, Australia.
Milo RadunskiCurtin Medical School, Curtin University, Perth, Australia.
Paul ElleryCurtin Medical School, Curtin University, Perth, Australia.ORCID 0000-0002-3124-8397
Beatriz Ir de OliveiraCurtin School of Allied Health, Curtin University, Perth, Australia.ORCID 0000-0002-8770-5075
Hannah RadleyCurtin Medical School, Curtin University, Perth, Australia.ORCID 0000-0002-3803-7314
Petra HelmholzSchool of Earth and Planetary Sciences, Curtin University, Perth, Australia.ORCID 0000-0001-9894-3119
Zhonghua SunCurtin Medical School, Curtin University, Perth, Australia.ORCID 0000-0002-7538-4761

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional (3D) representations of anatomical specimens are increasingly used as learning resources. Photogrammetry is a well-established technique that can be used to generate 3D models and has only been recently applied to produce visualisations of cadaveric specimens. This study has developed a semi-standardised photogrammetry workflow to produce photorealistic models of human specimens. Eight specimens, each with unique anatomical characteristics, were successfully digitised into interactive 3D models using the described workflow and the strengths and limitations of the technique are described. Various tissue types were reconstructed with apparent preservation of geometry and texture which visually resembled the original specimen. Using this workflow, an institution could digitise their existing cadaveric resources, facilitating the delivery of novel educational experiences.

Indexed as

PhotogrammetryCadaverHumansWorkflow3D visualisationanatomy visualisationcadaverphotogrammetry

Identifiers

PMID37432760
PMCPMC10335382

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.