PD41-08 SKILL ACQUISITION AND ITS RETENTION AFTER SIMULATION-BASED PRACTICE DURING ROBOT-ASSISTED SURGERY: CAN FUNCTIONAL BRAIN STATES HELP US FORGE FORWARD?

Abstract

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment II1 Apr 2017PD41-08 SKILL ACQUISITION AND ITS RETENTION AFTER SIMULATION-BASED PRACTICE DURING ROBOT-ASSISTED SURGERY: CAN FUNCTIONAL BRAIN STATES HELP US FORGE FORWARD? Somayeh Shafiei, Thomas Fiorica, Ahmed Hussein, Youssef Ahmed, Sarah Muldoon, and Khurshid Guru Somayeh ShafieiSomayeh Shafiei More articles by this author , Thomas FioricaThomas Fiorica More articles by this author , Ahmed HusseinAhmed Hussein More articles by this author , Youssef AhmedYoussef Ahmed More articles by this author , Sarah MuldoonSarah Muldoon More articles by this author , and Khurshid GuruKhurshid Guru More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1887AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Patient safety is fundamental to surgical practice and it is critical to ensure surgical training and competence. Little has been published on brain cognitive states during learning and retention of basic Robot-Assisted Surgical skills. We sought to evaluate the feasibility of utilizing a novel brain functional states to evaluate surgical competency. METHODS 27 medical students were evaluated while performing four key tasks of the validated Fundamental Skills of Robot Surgery (FSRS) Curriculum and one advanced surgical module - the Hands-on Surgical Training (HoST) over six sessions, utilizing the robotic Surgery Simulator (RoSS). The four FSRS tasks evaluated were - Instrument Control Task, Ball Placement Task, Spatial Control II Task, Threading string through a series of hoops and 4th Arm Tissue Retraction. Tool -based metrics were assessed and recorded by RoSS. Brain states are extracted using the pairwise phase synchronization between EEG channels and are presented as functional brain networks. The functional brain networks are then quantified using network statistics, and spectral density of signals for all channels (mental workload). RESULTS The average mental workload initially increases before significantly decreasing across sessions(Fig 1). This trend is also observed in functional brain states during the four tool-based metrics, as integration and segregation features increase at the beginning of learning and later decrease (Fig 2). We observed significant correlations between brain state and tool-based metrics (RoSS), while performing HOST task, where brain states do not correlate. CONCLUSIONS We report to our knowledge, the first study that evaluates brain states during skill acquisition and learning after simulation-based training. Various brain areas are functionally activated and integrated while acquiring new skills but these interactions decrease after preliminary learning. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e810 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Somayeh Shafiei More articles by this author Thomas Fiorica More articles by this author Ahmed Hussein More articles by this author Youssef Ahmed More articles by this author Sarah Muldoon More articles by this author Khurshid Guru More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...