Evidence map›Paper›PMID 29439539›Full record

ReviewSensors (Basel, Switzerland)2018

Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature.

Chongjun Yang, Yu Xie, Shuang Liu, Dong Sun

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Adaptive Grid Search Method Using Dynamic Step-Size Adjustment for Robot-Tissue Interaction Force Estimation.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
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  5. Review
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  8. Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023
    Article
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  17. Proposal of the Tactile Glove Device.Sensors (Basel, Switzerland) · 2019
    Article
  18. 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

4 authors.

Chongjun YangDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, China. yangchongjun@stu.xmu.edu.cn.ORCID 0000-0002-6629-2769
Yu XieDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, China. xieyu@xmu.edu.cn.
Shuang LiuSchool of Mechanical and Power Engineer, East China University of Science and Technology, Shanghai 200237, China. shuangliu@ecust.edu.cn.
Dong SunDepartment of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China. medsun@cityu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robot-assisted surgery is of growing interest in the surgical and engineering communities. The use of robots allows surgery to be performed with precision using smaller instruments and incisions, resulting in shorter healing times. However, using current technology, an operator cannot directly feel the operation because the surgeon-instrument and instrument-tissue interaction force feedbacks are lost during needle insertion. Advancements in force feedback and control not only help reduce tissue deformation and needle deflection but also provide the surgeon with better control over the surgical instruments. The goal of this review is to summarize the key components surrounding the force feedback and control during robot-assisted needle insertion. The literature search was conducted during the middle months of 2017 using mainstream academic search engines with a combination of keywords relevant to the field. In total, 166 articles with valuable contents were analyzed and grouped into five related topics. This survey systemically summarizes the state-of-the-art force control technologies for robot-assisted needle insertion, such as force modeling, measurement, the factors that influence the interaction force, parameter identification, and force control algorithms. All studies show force control is still at its initial stage. The influence factors, needle deflection or planning remain open for investigation in future.

Indexed as

RoboticsFeedbackHumansMechanical PhenomenaNeedlesSurveys and Questionnairesforce controlforce measurementforce modelingneedle insertionparameter identification

Identifiers

PMID29439539
PMCPMC5855056

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.