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Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions
Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993204/ https://www.ncbi.nlm.nih.gov/pubmed/32038218 http://dx.doi.org/10.3389/fnbot.2019.00108 |
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author | Bahar, Lidor Sharon, Yarden Nisky, Ilana |
author_facet | Bahar, Lidor Sharon, Yarden Nisky, Ilana |
author_sort | Bahar, Lidor |
collection | PubMed |
description | Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object—an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable. |
format | Online Article Text |
id | pubmed-6993204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69932042020-02-07 Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions Bahar, Lidor Sharon, Yarden Nisky, Ilana Front Neurorobot Neuroscience Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object—an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable. Frontiers Media S.A. 2020-01-24 /pmc/articles/PMC6993204/ /pubmed/32038218 http://dx.doi.org/10.3389/fnbot.2019.00108 Text en Copyright © 2020 Bahar, Sharon and Nisky. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bahar, Lidor Sharon, Yarden Nisky, Ilana Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title | Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title_full | Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title_fullStr | Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title_full_unstemmed | Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title_short | Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions |
title_sort | surgeon-centered analysis of robot-assisted needle driving under different force feedback conditions |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993204/ https://www.ncbi.nlm.nih.gov/pubmed/32038218 http://dx.doi.org/10.3389/fnbot.2019.00108 |
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