Evidence map›Paper›PMID 29445564›Full record

ArticleProceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics2016

3-DOF Force-Sensing Micro-Forceps for Robot-Assisted Membrane Peeling: Intrinsic Actuation Force Modeling.

Anzhu Gao, Berk Gonenc, Jiangzhen Guo, Hao Liu, Peter Gehlbach, Iulian Iordachita

Abstract read
In one paragraph

Article in Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

4 citing papers in PubMed, 18 citations in OpenAlex.

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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

6 authors at 3 institutions in 2 countries.

Anzhu GaoState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Berk GonencCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Jiangzhen GuoCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Hao LiuState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
Peter GehlbachWilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287 USA.
Iulian IordachitaCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Johns Hopkins University · USChinese Academy of Sciences · CNUniversity of Chinese Academy of Sciences · CN

Funding

An Active Handheld MicromanipulatorR01EB000526 · NIBIB · CARNEGIE-MELLON UNIVERSITY · PI RIVIERE, CAMERON N · 2003 to 2017
$2.6M
NIBIB NIH HHS R01 EB000526
6 · The paper itself

Abstract

Membrane peeling is a challenging procedure in retinal microsurgery, requiring careful manipulation of delicate tissues by using a micro-forceps and exerting very fine forces that are mostly imperceptible to the surgeon. Previously, we developed a micro-forceps with three integrated fiber Bragg grating (FBG) sensors to sense the lateral forces at the instrument's tip. However, importantly this architecture was insufficient to sense the tissue pulling forces along the forceps axis, which may be significant during membrane peeling. Our previous 3-DOF force sensing solutions developed for pick tools are not appropriate for forceps tools due to the motion and intrinsic forces that develop while opening/closing the forceps jaws. This paper presents a new design that adds another FBG attached to the forceps jaws to measure the axial loads. This involves not only the external tool-to-tissue interactions that we need to measure, but also the adverse effect of intrinsic actuation forces that arise due to the elastic deformation of jaws and friction. In this study, through experiments and finite element analyses, we model the intrinsic actuation force. We investigate the effect of the coefficient of friction and material type (stainless steel, titanium, nitinol) on this model. Then, the obtained model is used to separate the axial tool-to-tissue forces from the raw sensor measurements. Preliminary experiments and simulation results indicate that the developed linear model based on the actuation displacement is feasible to accurately predict the axial forces at the tool tip.

Identifiers

PMID29445564
PMCPMC5808445
OpenAlexW2506164035

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.