Evidence map›Paper›PMID 40963200›Full record

ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2025

Measuring Abdominal Skin-Surface Distances Using Photos for Perforator Mapping Analysis-A Validation Study on 3D-Printed DIEP-Flap Models.

Fabian N Necker, David J Cholok, Marc J Fischer, Kyle Gifford, Chris Le Castillo, Michael Scholz, Michael Januszyk, Christoph W Leuze, Bruce L Daniel, Arash Momeni

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 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

10 authors.

Fabian N NeckerDigital Anatomy Lab, Faculty of Medicine, Institute of Functional and Clinical Anatomy, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0002-4044-4330
David J CholokDivision of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0002-9570-120X
Marc J FischerDepartment of Radiology, Stanford IMMERS (Incubator for Medical Mixed and Extended Reality at Stanford), Stanford University School of Medicine, Palo Alto, California, USA.
Kyle GiffordDepartment of Radiology, 3D and Quantitative Imaging, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0001-7760-5575
Chris Le CastilloDepartment of Radiology, 3D and Quantitative Imaging, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-4261-6837
Michael ScholzDigital Anatomy Lab, Faculty of Medicine, Institute of Functional and Clinical Anatomy, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0001-8440-6785
Michael JanuszykDivision of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0002-0313-0973
Christoph W LeuzeDepartment of Radiology, Stanford IMMERS (Incubator for Medical Mixed and Extended Reality at Stanford), Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0003-4564-8014
Bruce L DanielDepartment of Radiology, Stanford IMMERS (Incubator for Medical Mixed and Extended Reality at Stanford), Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0003-0475-5892
Arash MomeniDivision of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0002-6452-751X

Funding

Bayerisch-Kalifornischen HochschulzentrumBayrisches Forschungsinstitut für Digitale Transformation
6 · The paper itself

Abstract

backgroundWe present a novel method for accurately measuring skin-surface distances using standard smartphone photos and Photoshop, validated on 3D-printed DIEP-flap models and on calibration grid-patterns. MATERIALS AND

methodsDistance measurements are acquired in Photoshop in a calibration plane between dots on a grid-pattern as well as between perforators on photos of 3D-printed models and compared against ground-truth. Margins of errors are calculated from fitted linear models.

resultsSubmillimeter accuracy can be achieved within errors of ±0.45 mm (80% probability) and ±0.8 mm (95% probability) for measuring distances on the dot-grid. On the 3D-printed DIEP-models, distance measurements are accurate within ±1.75 mm (80% probability) and ±3.1 mm (95% probability).

conclusionsWe introduce a simple yet highly accurate technique to measure skin-surface distances using normal photos. Depending on the scenario, submillimeter or conservatively very low millimetre errors can be achieved, sufficiently accurate for clinical use, whilst maintaining topographic relationships of the measurements.

Indexed as

AbdomenPerforator FlapPrinting, Three-DimensionalSkinCalibrationHumansModels, AnatomicPhotographyReproducibility of ResultsSmartphone3D‐printed DIEP‐flapsaccuracy validationperforator locationperforator mappingperspective‐corrected photographsskin‐surface measurements

Identifiers

PMID40963200
PMCPMC12444031

What OpenQuestion holds

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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.