Evidence map›Paper›PMID 40205122›Full record

ArticleUrolithiasis2025

Initial experience with augmented reality in planning renal access for PCNL.

Rinat Lasmanovich, Maayan Dagan, Orel Hemo, Shai Tejman-Yarden, Dorit E Zilberman, Oliana Vazhgovsky, Zohar A Dotan, Nir Kleinmann, Asaf Shvero

Abstract read
In one paragraph

Article in Urolithiasis, 2025. 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

9 authors.

Rinat LasmanovichDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel. Rinat.lasmanovich@sheba.health.gov.il.ORCID http://orcid.org/0000-0002-7605-4684
Maayan DaganThe Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel.
Orel HemoDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel.
Shai Tejman-YardenThe Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel.
Dorit E ZilbermanDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel.
Oliana VazhgovskyThe Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel.
Zohar A DotanDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel.
Nir KleinmannDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel.
Asaf ShveroDepartment of Urology, Sheba Medical Center, Ramat Gan, Israel.ORCID http://orcid.org/0000-0002-3688-5133

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving renal access during percutaneous nephrolithotomy is challenging, with a complication rate of 10-20%. This study aims to assess a novel technique for planning access to the renal collecting system using augmented reality (AR). Using Digital Imaging and Communications in Medicine software (DICOM 2-print), we generated 3-dimensional (3D) models of patients with different types of kidney stones (staghorn, lower pole, and pelvic stones) who underwent PCNL between 2018 and 2022. After viewing the patient's CT scans, surgeons explored the anatomical models using an AR system with a stereoscopic 3D lens array display. Using questionnaires, we quantitatively estimated the model's contribution on a scale of "1" ("poor") to "5" ("excellent") to the surgeon's understanding of stone location, patient anatomy, and ease of access compared to 2D CT imaging. A total of 38 questionnaires were completed by 13 urologists. Estimating renal location, renal pelvis, and stone mass were better demonstrated by AR (5 vs. 4, p < 0.001). The orientation of adjacent organs was better understood using AR (5 vs. 4, p = 0.004). In 86.8% of cases, surgeons preferred using AR models both before and during surgery, with 69.2% suggesting that AR could enhance procedural safety. The time frames for testing the models differed between senior and junior practitioners (p = 0.016) and were significantly reduced with model experience (p < 0.001). When comparing CT to 3D imaging, AR models provide a better understating of stone location, patient anatomy, and route of access to the collecting system. Further research is required to implement this innovative technique preoperatively and intra-operatively.

Indexed as

Augmented RealityKidney CalculiNephrolithotomy, PercutaneousAdultAgedFemaleHumansImaging, Three-DimensionalKidneyMaleMiddle AgedModels, AnatomicSurveys and QuestionnairesTomography, X-Ray ComputedAugmented realityPCNLPercutaneous nephrolithotomy, 3DThree-dimensional, AR

Identifiers

PMID40205122
PMCPMC11982134

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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.