The spread of robot-assisted minimal invasive surgery presents new challenges to surgeons and researchers alike. The novel surgical tools, methods and processes require different skills from the clinicians, thus surgical training and skillassessment has become increasingly important. In this article, we describe the modification process of a widely used training device, the Fundamentals of Robotic Surgery (FRS) Dome, that was made capable of automatic skill-assessment too, by applying different sensors on it. The basic principles and methods of the sensorization process are shown, together with the first results obtained on the amended training platform.

Background: This research paper provides a systematic literature review (SLR) on the current status of augmented-reality head-mounted devices (AR-HMDs) that guide and navigate spine surgeries and pedicle screw placement. Methods: Embase, Scopus, PubMed, Cochrane Library and IEEE Xplore databases were screened for the systematic literature search to collect and statistically analyze live patient clinical, procedural and user experience data. Multi-level Poisson and binominal models were used for analysis. Results: In vivo patient data, only the clinically widely used Gertzbein–Robbins Scale, were published as an outcome in the recent heterogeneous literature. The statistical analysis supports the hypothesis that using AR-HMDs has the same clinical outcomes as using more expensive robot-assisted surgical (RAS) systems. Conclusions: AR-HMD-guided pedicle screw insertion is reaching its technology readiness, providing similar benefits to RAS. Further meta-analysis is expected in the future from higher case-numbered and standardized randomized clinical trials.

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