Evidence map›Paper›PMID 40553296›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

A simulation environment for robot-assisted endovascular interventions.

Matteo Pescio, Chenhao Li, Dennis Kundrat, Maura Casadio, Giulio Dagnino

Abstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

5 authors.

Matteo PescioPolitecnico di Torino, Turin, Italy.ORCID http://orcid.org/0009-0001-3035-1392
Chenhao LiUniversity of Twente, Enschede, The Netherlands.
Dennis KundratUniversity of Twente, Enschede, The Netherlands.ORCID http://orcid.org/0000-0003-4260-1747
Maura CasadioUniversità degli Studi di Genova, Genoa, Italy.
Giulio DagninoUniversity of Twente, Enschede, The Netherlands. g.dagnino@utwente.nl.ORCID http://orcid.org/0000-0002-1613-1051

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCardiovascular diseases are the leading cause of mortality globally. Advances in interventional radiology and endovascular devices have made endovascular procedures effective alternatives to traditional open surgery, leading to their routine application in clinical practice. Within this framework, novel technologies, including robotic platforms and navigation software, have been developed to assist clinicians in executing endovascular interventions with improved dexterity, enhanced guidance, and superior clinical training, ultimately yielding better patient outcomes.

methodsThis study aims to develop a model-based simulation environment within the SOFA framework, to enable shape and force sensing for endovascular robotic procedures. The vascular catheter was modeled using beam theory, and realistic interactions between the catheter and vascular models were established using the finite element method (FEM) with both linear elastic and nonlinear hyper-elastic models. Experiments measured contact forces and positional changes during catheter insertion, comparing anatomical deformations with simulation results.

resultsExperimental tests validated the simulated force and displacement measurements. The catheter contact force showed an absolute error of 0.0371 N (30.45%). Catheter tip displacement averaged 3.1 mm, and the proximal segment's Fréchet distance averaged 3.6 mm. For the anatomical model, the elastic FEM model performed best, with deformation measurement errors of 34%, 19%, and 59% across three different force scenarios.

conclusionThe results indicate that the integration of advanced physical modeling, realistic human-robot interactions, and enhanced computational capabilities will facilitate the development of innovative solutions, enabling clinicians to achieve greater accuracy and reliability in minimally invasive surgical (MIS) applications, particularly in endovascular interventions.

Indexed as

Computer SimulationEndovascular ProceduresRobotic Surgical ProceduresFinite Element AnalysisHumansDigital twinEndovascular robotic surgeryModel-based force and shape sensingSurgical simulation

Identifiers

PMID40553296
PMCPMC12575544

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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.