Objective To develop a robotic surgery training regimen integrating objective skill assessment for otolaryngology and head and neck surgery trainees consisting of training modules of increasing complexity and leading up to procedure specific training. In particular, we investigate applications of such a training approach for surgical extirpation of oropharyngeal tumors via a transoral approach using the da Vinci Robotic system. Study Design Prospective blinded data collection and objective evaluation (OSATS) of three distinct phases using the da Vinci Robotic surgical system. Setting Academic University Medical Engineering/Computer Science laboratory Methods Between September 2010 and July 2011, 8 Otolaryngology Head and Neck Surgery residents and 4 staff “experts” from an academic hospital participated in three distinct phases of robotic surgery training involving 1) robotic platform operational skills, 2) set-up of the patient side system, and 3) a complete ex-vivo surgical extirpation of an oropharyngeal “tumor” located in the base of tongue. Trainees performed multiple (4) approximately equally spaced training sessions in each stage of the training. In addition to trainees, baseline performance data was obtained for the experts. Each surgical stage was documented with motion and event data captured from the application programming interfaces (API) of the da Vinci system, as well as separate video cameras as appropriate. All data was assessed using automated skill measures of task efficiency, and correlated with structured assessment (OSATS, and similar Likert scale) from three experts to assess expert and trainee differences, and compute automated and expert assessed learning curves. Results Our data shows that such training results in an improved didactic robotic knowledge base and improved clinical efficiency with respect to the set-up and console manipulation. Experts (e.g. average OSATS 25, Stdev. 3.1, module 1 – suturing) and trainees (average OSATS 15.9, Stdev. 3.9, week 1) are well separated at the beginning of the training, and the separation reduces significantly (expert average OSATS 27.6, Std. 2.7, trainee average OSATS 24.2, Std. 6.8, module 3) at the conclusion of the training. Learning curves in each of the three stages show diminishing differences between the experts and trainees, also consistent with expert assessment. Subjective assessment by experts verified the clinical utility of the module 3 surgical environment and a survey of trainees consistently rated the curriculum as very useful in progression to human operating room assistance. Conclusions Structured curricular robotic surgery training with objective assessment promises to reduce the overhead for mentors, allow detailed assessment of human-machine interface skills and create customized training models for individualized training. This preliminary study verifies the utility of such training in improving human-machine operations skills (module 1), and operating room and surgical skills (module 2 and 3). In contrast to cur...
The Robo-ELF is a novel robot to assist in driving a flexible endoscope for surgery of the upper aerodigestive tract.
This paper describes the continued development of the Robotic EndoLaryngeal (Robo-ELF) Scope System, a simple clinically usable robot for manipulating flexible endoscopes, particularly in laryngeal surgery. The system includes a robot with three active and two passive degrees of freedom, a five degree of freedom passive positioning arm, a malleable scope shaft support, and a custom joystick controller. The Robo-ELF Scope allows a surgeon to control a flexible endoscope with only one hand and also to release the controls and perform bimanual surgery if desired. We have evaluated the Robo-ELF Scope system in both phantom and cadaver studies and found it superior to hand manipulation of flexible endoscopes and conventional rigid endoscopes.
In Reply:We thank the readers for their careful consideration and review of our study and are encouraged by their sincere feedback.To update the readers, we are now preparing the next iteration of a curricular resident training protocol and have expanded the training beyond the base of tongue resection already published. We expect to provide the community with additional publications and updates when the next iteration is complete and results are available. In addition, our methods (and even equipment and resources where possible) are now available to the research community. Our work has also been reviewed by the Minimally Invasive Robotic Association Fundamentals of Robotic Surgery workgroup, and we have provided didactic input to that effort, which might someday be the accepted credentialing platform across specialties. In addition, we continue to mine our data for additional insights.With respect to the specific suggestions made by the readers, they are good advice for any research study. However, the presented study was well controlled for the effects of additional instruction. No instruction for the specific training tasks was provided during the training modules, and no additional disclosures are needed. We do agree that reviewing the design for needed controls and sources of bias is sound advice for any research study.We disagree with the interpretation and commentary on interobserver agreement. Our reviewers were well trained with pregraded example studies, and the rater training process was sound. The 0.8 "good" level (noted in the readers' letter) finds little support in broader analysis, and we used broadly accepted levels to classify our variability results. The levels measured are suitable for instructional use. We further note that these statistics (will) continue to evolve with data from additional protocols (both users and raters), and we will report such updates when available. It was not an aim of the study to improve rater agreement, we merely reported the levels achieved in this iteration of the protocol. It must be emphasized that the order of detail and complexity of our measurements and analysis is orders of magnitude greater than previous reports, and a comparison may be moot to begin with.We note that learning curves reported are for an individual trainee over the considerable duration of the protocol and are compared with expert performance. The additional statistical analysis recommended, although relevant, deviates from the primary goals of the first iteration of our protocol. We aimed to establish performance baselines and verify the efficacy of measurement and analysis methods, and that expert baselines were achievable for our experimental tasks given the various constraints (including time available) for today's trainees.In summary, we again thank the writers for their feedback. We will incorporate additional statistics, over additional raters and trainees, and over the following iterations of our protocol in follow-up publications. We also encourage this group, and other researcher...
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