Evidence map›Paper›PMID 42717850›Full record

ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2026

Entry Point Localisation for Percutaneous Surgical Robots Based on Concentric Fiducial Patches and Robust Surface Fitting.

Youming Deng, Qi Jiang, Jie Wang, Jiawei Tang

Abstract read
In one paragraph

Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2026. 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
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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

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

4 authors.

Youming DengSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, China.ORCID https://orcid.org/0009-0007-1199-3772
Qi JiangSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, China.ORCID https://orcid.org/0000-0003-3180-5701
Jie WangSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, China.ORCID https://orcid.org/0009-0006-1243-4326
Jiawei TangSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, China.

Funding

Major Scientific and Technological Innovation Project of Shandong Province 2024CXGC010321Natural Science Foundation of Guangdong Province 2025A1515012038Natural Science Foundation of Shandong Province ZR2024MF076
6 · The paper itself

Abstract

backgroundAccurate entry point localisation is critical for the safety of robot-assisted percutaneous interventions (RAPI). However, medical optical tracking systems for the traditional fiducial patch are cost-prohibitive. To address these challenges, this paper proposes a robust entry point and gesture localisation framework based on an inexpensive concentric fiducial patch and robust surface fitting.

methodsWe introduce an anisotropic iteratively reweighted least squares (IRLS) algorithm integrated with a Tukey biweight M-estimator, employing a deterministic soft-weighting strategy to effectively suppress gross outliers and smooth out high-frequency sensor noise.

resultsExperimental results demonstrate that the proposed method has high accuracy and robustness, achieving an MAE of 1.47 mm in dynamic respiratory tracking and maintains stable detection even under 60% occlusion and low-light conditions.

conclusionsThe results suggest that this framework provides a reliable and low-cost solution for surgical navigation in unstructured clinical environments.

Indexed as

Fiducial MarkersRobotic Surgical ProceduresSurgery, Computer-AssistedAlgorithmsAnisotropyHumansLeast-Squares AnalysisRespirationanisotropic iteratively reweighted least squares (IRLS)concentric fiducial patchRGB‐D sensingsurface fittingsurgical robot localisation

Identifiers

PMID42717850
PMCPMC13559500

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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.