Cargando…
Temporal clustering of surgical activities in robot-assisted surgery
PURPOSE: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clusterin...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509863/ https://www.ncbi.nlm.nih.gov/pubmed/28477279 http://dx.doi.org/10.1007/s11548-017-1600-y |
_version_ | 1783250088741568512 |
---|---|
author | Zia, Aneeq Zhang, Chi Xiong, Xiaobin Jarc, Anthony M. |
author_facet | Zia, Aneeq Zhang, Chi Xiong, Xiaobin Jarc, Anthony M. |
author_sort | Zia, Aneeq |
collection | PubMed |
description | PURPOSE: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery. METHODS: We collected system kinematics and events data from nine surgeons performing five different surgical tasks on a porcine model using the da Vinci Si surgical system. The five tasks were treated as one ‘pseudo-procedure.’ We compared four different temporal clustering algorithms—hierarchical aligned cluster analysis (HACA), aligned cluster analysis (ACA), spectral clustering (SC), and Gaussian mixture model (GMM)—using multiple feature sets. RESULTS: HACA outperformed the other methods reaching an average segmentation accuracy of [Formula: see text] when using all system kinematics and events data as features. SC and ACA reached moderate performance with [Formula: see text] and [Formula: see text] average segmentation accuracy, respectively. GMM consistently performed poorest across algorithms. CONCLUSIONS: Unsupervised temporal segmentation of surgical procedures into clinically relevant steps achieves good accuracy using just system data. Such methods will enable surgeons to receive directed feedback on individual surgical tasks rather than whole procedures in order to improve workflow, assessment, and training. |
format | Online Article Text |
id | pubmed-5509863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-55098632017-07-28 Temporal clustering of surgical activities in robot-assisted surgery Zia, Aneeq Zhang, Chi Xiong, Xiaobin Jarc, Anthony M. Int J Comput Assist Radiol Surg Original Article PURPOSE: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery. METHODS: We collected system kinematics and events data from nine surgeons performing five different surgical tasks on a porcine model using the da Vinci Si surgical system. The five tasks were treated as one ‘pseudo-procedure.’ We compared four different temporal clustering algorithms—hierarchical aligned cluster analysis (HACA), aligned cluster analysis (ACA), spectral clustering (SC), and Gaussian mixture model (GMM)—using multiple feature sets. RESULTS: HACA outperformed the other methods reaching an average segmentation accuracy of [Formula: see text] when using all system kinematics and events data as features. SC and ACA reached moderate performance with [Formula: see text] and [Formula: see text] average segmentation accuracy, respectively. GMM consistently performed poorest across algorithms. CONCLUSIONS: Unsupervised temporal segmentation of surgical procedures into clinically relevant steps achieves good accuracy using just system data. Such methods will enable surgeons to receive directed feedback on individual surgical tasks rather than whole procedures in order to improve workflow, assessment, and training. Springer International Publishing 2017-05-05 2017 /pmc/articles/PMC5509863/ /pubmed/28477279 http://dx.doi.org/10.1007/s11548-017-1600-y Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Zia, Aneeq Zhang, Chi Xiong, Xiaobin Jarc, Anthony M. Temporal clustering of surgical activities in robot-assisted surgery |
title | Temporal clustering of surgical activities in robot-assisted surgery |
title_full | Temporal clustering of surgical activities in robot-assisted surgery |
title_fullStr | Temporal clustering of surgical activities in robot-assisted surgery |
title_full_unstemmed | Temporal clustering of surgical activities in robot-assisted surgery |
title_short | Temporal clustering of surgical activities in robot-assisted surgery |
title_sort | temporal clustering of surgical activities in robot-assisted surgery |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509863/ https://www.ncbi.nlm.nih.gov/pubmed/28477279 http://dx.doi.org/10.1007/s11548-017-1600-y |
work_keys_str_mv | AT ziaaneeq temporalclusteringofsurgicalactivitiesinrobotassistedsurgery AT zhangchi temporalclusteringofsurgicalactivitiesinrobotassistedsurgery AT xiongxiaobin temporalclusteringofsurgicalactivitiesinrobotassistedsurgery AT jarcanthonym temporalclusteringofsurgicalactivitiesinrobotassistedsurgery |