Evidence map›Paper›PMID 28736508›Full record

ArticleIEEE sensors journal2017

3-DOF Force-Sensing Motorized Micro-Forceps for Robot-Assisted Vitreoretinal Surgery.

Berk Gonenc, Alireza Chamani, James Handa, Peter Gehlbach, Russell H Taylor, Iulian Iordachita

Abstract read
In one paragraph

Article in IEEE sensors journal, 2017. 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
5.6field-weighted citation impact, top 4% 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

18 citing papers in PubMed, 68 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. A Review of Recent Advancements in Sensor-Integrated Medical Tools.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Artificial intelligence, robotics and eye surgery: are we overfitted?International journal of retina and vitreous · 2019
    Article
  17. Real-Time Sclera Force Feedback for Enabling Safe Robot-Assisted Vitreoretinal Surgery.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018
    Article
  18. Safe Tissue Manipulation in Retinal Microsurgery via Motorized Instruments with Force Sensing.Proceedings of IEEE Sensors. IEEE International Conference on Sensors
    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

6 authors at 1 institution in 1 country.

Berk GonencCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Alireza ChamaniCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
James HandaWilmer Eye Institute at The Johns Hopkins School of Medicine, Baltimore, MD 21287 USA.
Peter GehlbachWilmer Eye Institute at The Johns Hopkins School of Medicine, Baltimore, MD 21287 USA.
Russell H TaylorCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Iulian IordachitaCISST ERC at Johns Hopkins University, Baltimore, MD 21218 USA.
Johns Hopkins University · US

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

In vitreoretinal surgery, membrane peeling is a prototypical task where a layer of fibrous tissue is delaminated off the retina with a micro-forceps by applying very fine forces that are mostly imperceptible to the surgeon. Previously we developed sensitized ophthalmic surgery tools based on fiber Bragg grating (FBG) strain sensors, which were shown to precisely detect forces at the instrument's tip in two degrees of freedom perpendicular to the tool axis. This paper presents a new design that employs an additional sensor to capture also the tensile force along the tool axis. The grasping functionality is provided via a compact motorized unit. To compute forces, we investigate two distinct fitting methods: a linear regression and a nonlinear fitting based on second-order Bernstein polynomials. We carry out experiments to test the repeatability of sensor outputs, calibrate the sensor and validate its performance. Results demonstrate sensor wavelength repeatability within 2 pm. Although the linear method provides sufficient accuracy in measuring transverse forces, in the axial direction it produces a root mean square (rms) error over 3 mN even for a confined magnitude and direction of forces. On the other hand, the nonlinear method provides a more consistent and accurate measurement of both the transverse and axial forces for the entire force range (0-25 mN). Validation including random samples shows that our tool with the nonlinear force computation method can predict 3-D forces with an rms error under 0.15 mN in the transverse plane and within 2 mN accuracy in the axial direction.

Indexed as

fiber Bragg gratingforce sensingmicro-forceps

Identifiers

PMID28736508
PMCPMC5515300
OpenAlexW2606215192

What OpenQuestion holds

Textmetadata
LicenceTDM
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.