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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-01-01
|
| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnbot.2019.00108/full |
| _version_ | 1818536563834880000 |
|---|---|
| author | Lidor Bahar Yarden Sharon Ilana Nisky |
| author_facet | Lidor Bahar Yarden Sharon Ilana Nisky |
| author_sort | Lidor Bahar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T18:39:34Z |
| format | Article |
| id | doaj.art-219ff775b2b34caebaeaea34502ddfd9 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-11T18:39:34Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-219ff775b2b34caebaeaea34502ddfd92022-12-22T00:54:39ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182020-01-011310.3389/fnbot.2019.00108482081Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback ConditionsLidor BaharYarden SharonIlana NiskyRobotic 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.https://www.frontiersin.org/article/10.3389/fnbot.2019.00108/fullteleoperationforce feedbackneedle drivingrobot assisted minimally invasive surgerylearning |
| spellingShingle | Lidor Bahar Yarden Sharon Ilana Nisky Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions Frontiers in Neurorobotics teleoperation force feedback needle driving robot assisted minimally invasive surgery learning |
| 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 | teleoperation force feedback needle driving robot assisted minimally invasive surgery learning |
| url | https://www.frontiersin.org/article/10.3389/fnbot.2019.00108/full |
| work_keys_str_mv | AT lidorbahar surgeoncenteredanalysisofrobotassistedneedledrivingunderdifferentforcefeedbackconditions AT yardensharon surgeoncenteredanalysisofrobotassistedneedledrivingunderdifferentforcefeedbackconditions AT ilananisky surgeoncenteredanalysisofrobotassistedneedledrivingunderdifferentforcefeedbackconditions |