title: Kinematics effectively delineate accomplished users of endovascular robotics with a physical training model.
abstract: OBJECTIVE
  Endovascular robotics systems, now approved for clinical use in the United States and Europe, are seeing rapid growth in interest. Determining who has sufficient expertise for safe and effective clinical use remains elusive. Our aim was to analyze performance on a robotic platform to determine what defines an expert user.
  
  
  METHODS
  During three sessions, 21 subjects with a range of endovascular expertise and endovascular robotic experience (novices <2 hours to moderate-extensive experience with >20 hours) performed four tasks on a training model. All participants completed a 2-hour training session on the robot by a certified instructor. Completion times, global rating scores, and motion metrics were collected to assess performance. Electromagnetic tracking was used to capture and to analyze catheter tip motion. Motion analysis was based on derivations of speed and position including spectral arc length and total number of submovements (inversely proportional to proficiency of motion) and duration of submovements (directly proportional to proficiency).
  
  
  RESULTS
  Ninety-eight percent of competent subjects successfully completed the tasks within the given time, whereas 91% of noncompetent subjects were successful. There was no significant difference in completion times between competent and noncompetent users except for the posterior branch (151 s:105 s; P = .01). The competent users had more efficient motion as evidenced by statistically significant differences in the metrics of motion analysis. Users with >20 hours of experience performed significantly better than those newer to the system, independent of prior endovascular experience.
  
  
  CONCLUSIONS
  This study demonstrates that motion-based metrics can differentiate novice from trained users of flexible robotics systems for basic endovascular tasks. Efficiency of catheter movement, consistency of performance, and learning curves may help identify users who are sufficiently trained for safe clinical use of the system. This work will help identify the learning curve and specific movements that translate to expert robotic navigation.
authors.affiliation: Department of Cardiovascular Surgery, Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Tex. Electronic address: caduran@houstonmethodist.org.
authors.forename: Cassidy
authors.initials: C
authors.lastname: Duran
authors.affiliation: Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, Tex.
authors.forename: Sean
authors.initials: S
authors.lastname: Estrada
authors.affiliation: Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, Tex.
authors.forename: Marcia
authors.initials: M
authors.lastname: O'Malley
authors.affiliation: Department of Cardiovascular Surgery, Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Tex.
authors.forename: Alan B
authors.initials: AB
authors.lastname: Lumsden
authors.affiliation: Department of Cardiovascular Surgery, Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Tex.
authors.forename: Jean
authors.initials: J
authors.lastname: Bismuth
mesh_terms: D001696:Biomechanical Phenomena; D002983:Clinical Competence; D004503:Education, Medical, Graduate / Q000379:methods*; D057510:Endovascular Procedures / Q000193:education* / Q000295:instrumentation; D004867:Equipment Design; D005260:Female; D006801:Humans; D059032:Learning Curve; D008297:Male; D009038:Motion; D009048:Motor Skills; D012371:Robotics / Q000193:education* / Q000295:instrumentation; D025321:Surgery, Computer-Assisted / Q000193:education* / Q000295:instrumentation; D013647:Task Performance and Analysis