Evidence map›Paper›PMID 36686214›Full record

ArticleFrontiers in robotics and AI2022

Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery.

A McDonald-Bowyer, S Dietsch, E Dimitrakakis, J M Coote, L Lindenroth, D Stoyanov, A Stilli

Open access · goldAbstract read
In one paragraph

Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. 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

7 authors at 3 institutions in 1 country.

A McDonald-BowyerWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
S DietschWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
E DimitrakakisWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
J M CooteWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
L LindenrothDepartment of Surgical & Interventional Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
D StoyanovWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
A StilliWelcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.
University College London · GBWellcome / EPSRC Centre for Interventional and Surgical Sciences · GBKing's College London · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

In robotic-assisted partial nephrectomy, surgeons remove a part of a kidney often due to the presence of a mass. A drop-in ultrasound probe paired to a surgical robot is deployed to execute multiple swipes over the kidney surface to localise the mass and define the margins of resection. This sub-task is challenging and must be performed by a highly-skilled surgeon. Automating this sub-task may reduce cognitive load for the surgeon and improve patient outcomes. The eventual goal of this work is to autonomously move the ultrasound probe on the surface of the kidney taking advantage of the use of the Pneumatically Attachable Flexible (PAF) rail system, a soft robotic device used for organ scanning and repositioning. First, we integrate a shape-sensing optical fibre into the PAF rail system to evaluate the curvature of target organs in robotic-assisted laparoscopic surgery. Then, we investigate the impact of the PAF rail's material stiffness on the curvature sensing accuracy, considering that soft targets are present in the surgical field. We found overall curvature sensing accuracy to be between 1.44% and 7.27% over the range of curvatures present in adult kidneys. Finally, we use shape sensing to plan the trajectory of the da Vinci surgical robot paired with a drop-in ultrasound probe and autonomously generate an Ultrasound scan of a kidney phantom.

Indexed as

da Vinci research kit (dVRK)medical roboticsshape sensingsoft robot applicationssoft robot materials and designsoft sensors and actuatorssurgical robotics

Identifiers

PMID36686214
PMCPMC9849801
OpenAlexW4313550105

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.