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Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting
BACKGROUND: Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new sy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733873/ https://www.ncbi.nlm.nih.gov/pubmed/33751213 http://dx.doi.org/10.1007/s00464-021-08445-7 |
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author | Zhu, Toby S. Godse, Neal Clayburgh, Daniel R. Duvvuri, Umamaheswar |
author_facet | Zhu, Toby S. Godse, Neal Clayburgh, Daniel R. Duvvuri, Umamaheswar |
author_sort | Zhu, Toby S. |
collection | PubMed |
description | BACKGROUND: Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption. METHODS: Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot. RESULTS: 13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable. CONCLUSION: Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-021-08445-7. |
format | Online Article Text |
id | pubmed-8733873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87338732022-01-26 Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting Zhu, Toby S. Godse, Neal Clayburgh, Daniel R. Duvvuri, Umamaheswar Surg Endosc Article BACKGROUND: Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption. METHODS: Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot. RESULTS: 13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable. CONCLUSION: Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-021-08445-7. Springer US 2021-03-22 2022 /pmc/articles/PMC8733873/ /pubmed/33751213 http://dx.doi.org/10.1007/s00464-021-08445-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhu, Toby S. Godse, Neal Clayburgh, Daniel R. Duvvuri, Umamaheswar Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title_full | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title_fullStr | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title_full_unstemmed | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title_short | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
title_sort | assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733873/ https://www.ncbi.nlm.nih.gov/pubmed/33751213 http://dx.doi.org/10.1007/s00464-021-08445-7 |
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