Evidence map›Paper›PMID 30631976›Full record

ArticleBiomedical microdevices2019

Biaxial sensing suture breakage warning system for robotic surgery.

Yuan Dai, A Abiri, J Pensa, S Liu, O Paydar, H Sohn, S Sun, P A Pellionisz, C Pensa, E P Dutson and 2 more

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

Article in Biomedical microdevices, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 24% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Artificial intelligence: revolutionizing robotic surgery: review.Annals of medicine and surgery (2012) · 2024
    Review
  6. Review
  7. 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

12 authors at 2 institutions in 1 country.

Yuan DaiDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0002-6705-1342
A AbiriCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
J PensaCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
S LiuDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA.
O PaydarCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
H SohnDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA.
S SunCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
P A PellioniszCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
C PensaDepartment of Biomedical Engineering, University of California, Los Angeles, CA, 90095, USA.
E P DutsonCenter for Advanced Surgical and Interventional Technology (CASIT), Los Angeles, CA, 90095, USA.
W S GrundfestDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA.
R N CandlerDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA. rcandler@g.ucla.edu.
University of California, Los Angeles · USCalifornia NanoSystems Institute · US

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Foundation for the National Institutes of Health R01EB019473-01NIGMS NIH HHS T32 GM008042
6 · The paper itself

Abstract

The number of procedures performed with robotic surgery may exceed one million globally in 2018. The continual lack of haptic feedback, however, forces surgeons to rely on visual cues in order to avoid breaking sutures due to excessive applied force. To mitigate this problem, the authors developed and validated a novel grasper-integrated system with biaxial shear sensing and haptic feedback to warn the operator prior to anticipated suture breakage. Furthermore, the design enables facile suture manipulation without a degradation in efficacy, as determined via measured tightness of resulting suture knots. Biaxial shear sensors were integrated with a da Vinci robotic surgical system. Novice subjects (n = 17) were instructed to tighten 10 knots, five times with the Haptic Feedback System (HFS) enabled, five times with the system disabled. Seven suture failures occurred in trials with HFS enabled while seventeen occurred in trials without feedback. The biaxial shear sensing system reduced the incidence of suture failure by 59% (p = 0.0371). It also resulted in 25% lower average applied force in comparison to trials without feedback (p = 0.00034), which is relevant because average force was observed to play a role in suture breakage (p = 0.03925). An observed 55% decrease in standard deviation of knot quality when using the HFS also indicates an improvement in consistency when using the feedback system. These results suggest this system may improve outcomes related to knot tying tasks in robotic surgery and reduce instances of suture failure while not degrading the quality of knots produced.

Indexed as

Materials TestingSuturesTensile StrengthHumansRobotic Surgical ProceduresDa VinciForce sensorHaptic feedbackHapticsRobotic surgeryShear sensor

Identifiers

PMID30631976
PMCPMC6545119
OpenAlexW2910459763

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.