Evidence map›Paper›PMID 29628753›Full record

ArticleIEEE/ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division2017

Motorized Micro-Forceps with Active Motion Guidance based on Common-Path SSOCT for Epiretinal Membranectomy.

Gyeong Woo Cheon, Berk Gonenc, Russell H Taylor, Peter L Gehlbach, Jin U Kang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE/ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

10 citing papers in PubMed, 16 citations in OpenAlex.

  1. Magnification-Invariant Retinal Distance Estimation for Vitreoretinal Surgery Using a Laser Aiming Beam.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025
    Article
  2. Review
  3. 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
  4. Article
  5. Article
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  7. Stochastic Force-based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery.IEEE/ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division · 2021
    Article
  8. 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 at 1 institution in 1 country.

Gyeong Woo CheonDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Berk GonencERC for Computer Integrated Surgery at Johns Hopkins University, Baltimore, MD, USA.
Russell H TaylorDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Peter L GehlbachWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Jin U KangDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins University · US

Funding

OCT-Guided Free-Hand Semi-Automated Microsurgical Tool for Enhanced Retinal SurgeR01EY021540 · NEI · JOHNS HOPKINS UNIVERSITY · PI GEHLBACH, PETER LOUIS, KANG, JIN U · 2011 to 2015
$2.0M
NEI NIH HHS R01 EY021540
6 · The paper itself

Abstract

In this study, we built and tested a handheld motion-guided micro-forceps system using common-path swept source optical coherence tomography (CP-SSOCT) for highly accurate depth controlled epiretinal membranectomy. A touch sensor and two motors were used in the forceps design to minimize the inherent motion artifact while squeezing the tool handle to actuate the tool and grasp, and to independently control the depth of the tool-tip. A smart motion monitoring and a guiding algorithm were devised to provide precise and intuitive freehand control. We compared the involuntary tool-tip motion occurring while grasping with a standard manual micro-forceps and our touch sensor activated micro-forceps. The results showed that our touch-sensor-based and motor-actuated tool can significantly attenuate the motion artifact during grasping (119.81 μm with our device versus 330.73 μm with the standard micro-forceps). By activating the CP-SSOCT based depth locking feature, the erroneous tool-tip motion can be further reduced down to 5.11μm. We evaluated the performance of our device in comparison to the standard instrument in terms of the elapsed time, the number of grasping attempts, and the maximum depth of damage created on the substrate surface while trying to pick up small pieces of fibers (Ø 125 μm) from a soft polymer surface. The results indicate that all metrics were significantly improved when using our device; of note, the average elapsed time, the number of grasping attempts, and the maximum depth of damage were reduced by 25%, 31%, and 75%, respectively.

Indexed as

Biomedical optical imagingImage sensorsOptical signal detectionOptical signal processingSurgery

Identifiers

PMID29628753
PMCPMC5881930
OpenAlexW2753134805

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.