Motion Capture as a Tool for the Assessment of Laparoscopic Performance

Overview

The goal of this study is to 1) identify motion capture variables that can be used to differentiate surgical experience level and 2) evaluate if these variables can be used in a surgical education setting to improve resident performance.

This will be a two-phase study that will take 2 years to complete. Phase 1 will recruit staff surgeons, surgical fellows, and residents from the departments of Obstetrics and Gynecology, Urology, General Surgery, and Thoracics at The University of Ottawa and The Ottawa Hospital to complete a set of FLS tasks. Logistic regressions will be used to identify significant factors that differentiate surgical experience level (junior residents, senior residents, fellows, and staff). Classification of residents will depend on their current cohort in the CDB or traditional time-based system: A target interval will be computed for each significant factor based on the staff surgeons' average performance. This target interval will be used to assess resident performance in Phase 2. Phase 2 will recruit Transition to Discipline residents from each department to complete 3 sets of FLS tasks. Participants will be randomized into an "objective feedback" group or a "no feedback" group. Participants randomized to the objective feedback group will receive a report that compares their performance in Set 1 to that of the staff surgeons' using the target interval as a reference. Participants receiving objective feedback will then complete Sets 2 and 3 knowing what factors to improve upon. Participants receiving no feedback will complete all sets with no intervention. Phase 2 data collection will start once Phase 1 is completed. Trajectories of retro-reflective markers will be recorded using a 6-camera motion analysis system (40MXF-40 Vicon cameras, Oxford Metrics, Oxford, UK) sampling at 100 Hz with supporting Nexus software (v2, Oxford Metrics, Oxford, UK). Retro-reflective markers will be placed on various anatomical landmarks on the participant's body based on a cluster marker set. Laparoscopic surgical tools (grasper, scissors, or dissector) will have a marker triad fixed to the upper shaft while a laparoscopic trainer (FLS Trainer System, Limbs \& Things, Savannah GA) will have markers placed at its corners. Using Vicon Nexus software, raw marker trajectories will be filtered and used to compute local coordinate systems of the upper body, joint centers, and subsequent joint angles while filtered marker trajectories on the laparoscopic tools will compute the tool tip's motion and orientation relative to the box trainer's workspace. Inertial measurement units (IMUs) will also be used to capture motion of the upper body and compare to motion capture outcomes (Secondary Objective). Wireless IMUs will be placed along the long axis of the forearm, upper arm, upper spine and lower spine. Surface electromyography (EMG) signals of the biceps, triceps, anterior deltoid, upper trapezius, wrist extensors and flexors will also be recorded using a wireless EMG system (Trigno, Delsys Inc., Natick MA) and recorded using Nexus. Sensors will be placed over the muscle bellies following SENIAM guidelines \[34\] and signals will be sample data 100Hz with a 20-450Hz bandpass. All experimental EMG signals will be appropriately conditioned and normalized to maximum EMG value elicited during maximum voluntary isometric contraction (MVIC) exercises. Three repetitions of each MVIC exercise will be performed before the experimental task and will include elbow flexion and extension, arm abduction, arm flexion, and wrist flexion and extension. A minimum of 30 seconds rest will be given between each exercise to mitigate effects of fatigue. Motion and EMG data will be exported to a custom MatLab (2019, Mathworks, Natick, MA) application to compute objective measures of laparoscopic performance: instrument tip pathlength, average (±1 stdev) excursion velocity, average (±1 stdev) excursion error, average (±1 stdev) upper body kinematics (shoulder, elbow, wrist, neck and trunk) in all three planes of motion, and integrated EMG. Video of the surgical tasks will be recorded and synced to marker trajectory data using an external trigger. Videos will be de-identified, and the last repetition assessed by three staff surgeons. First, the raters will categorize each video as Transition to Discipline resident, Foundation of Discipline resident, Core Discipline resident, Transition to Practice resident, fellow, or staff surgeon based other their general perception of the performer's experience level. The raters will then score each video following the FLS scoring system (based on timing and penalty scores), and the OSATS evaluation grid.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes