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

McDonald-Bowyer, A; Dietsch, S; Dimitrakakis, E; Coote, JM; Lindenroth, L; Stoyanov, D; Stilli, A; (2023) Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery. Frontiers in Robotics and AI , 9 , Article 1099275. 10.3389/frobt.2022.1099275. Green open access

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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.

Type: Article
Title: Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/frobt.2022.1099275
Publisher version: https://doi.org/10.3389/frobt.2022.1099275
Language: English
Additional information: © 2023 McDonald-Bowyer, Dietsch, Dimitrakakis, Coote, Lindenroth, Stoyanov and Stilli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: da Vinci research kit (dVRK), medical robotics, shape sensing, soft robot applications, soft robot materials and design, soft sensors and actuators, surgical robotics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10164176
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