ArticleInternational journal of computer assisted radiology and surgery2017
Temporal clustering of surgical activities in robot-assisted surgery.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Robotics-assisted versus conventional manual approaches for total hip arthroplasty: A systematic review and meta-analysis of comparative studies.The international journal of medical robotics + computer assisted surgery : MRCAS · 2019Pooled it
- Active control time: an objective performance metric for trainee participation in robotic surgery.Journal of robotic surgery · 2023Article
- Deep neural network architecture for automated soft surgical skills evaluation using objective structured assessment of technical skills criteria.International journal of computer assisted radiology and surgery · 2023Article
- Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.Sensors (Basel, Switzerland) · 2022Review
- Bidirectional long short-term memory for surgical skill classification of temporally segmented tasks.International journal of computer assisted radiology and surgery · 2020Article
- How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery.Visceral medicine · 2020Article
- Novel evaluation of surgical activity recognition models using task-based efficiency metrics.International journal of computer assisted radiology and surgery · 2019Article
- Video and accelerometer-based motion analysis for automated surgical skills assessment.International journal of computer assisted radiology and surgery · 2018Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMost 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.
methodsWe 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.
resultsHACA 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.
conclusionsUnsupervised 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.
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