Monica A Farcas1, Maeve O'Neill Trudeau1, Ahmed Nasr2, J Ted Gerstle1,3, Brian Carrillo3, Georges Azzie4,5. 1. Department of Surgery, University of Toronto, Toronto, ON, Canada. 2. Division of Pediatric Surgery, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada. 3. Division of General and Thoracic Surgery, Center for Image Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. 4. Department of Surgery, University of Toronto, Toronto, ON, Canada. georges.azzie@sickkids.ca. 5. Division of General and Thoracic Surgery, Center for Image Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. georges.azzie@sickkids.ca.
Abstract
BACKGROUND: This study analyzes instrument motion for segments of a defined intra-corporeal suturing task in a laparoscopic simulator. We describe a system providing real-time velocity and acceleration assessment in the performance of this task. Analysis of the deconstructed task segments allows targeted assessment and teaching. METHODS: A traditional box trainer was fitted with a custom-built motion-tracking system. Participants were stratified into novice, intermediate and expert groups. They performed a defined intra-corporeal suturing task. Real-time data were collected in four degrees of freedom (DOFs) (Roll, Surge, Pitch, Yaw). The task was then deconstructed into four segments: loading needle/pull-through, double-throw knot, first single-throw knot, and second single-throw knot. Motion analysis parameters (MAPs) were studied for each DOF. RESULTS: Sixty-four participants were tested (14 novices, 19 intermediates, 31 experts). The largest difference in MAPs was seen in the 'double-throw knot' segment. MAPs for the 'loading needle/pull-through' segment revealed differences between novices and experts in Roll and Pitch DOFs only. For the 'first single knot' segment, similar MAP trends were noted across all DOFs, with significant differences between novices versus experts and intermediates versus experts. For the 'second single knot' segment, the difference in MAPs was preserved only for novices versus experts. CONCLUSIONS: By analyzing motion for a defined suturing task in a laparoscopic simulator, we can gain insight into the specific hand motions distinguishing experts from non-experts. Such information may allow teaching in a more focused, effective and efficient manner.
BACKGROUND: This study analyzes instrument motion for segments of a defined intra-corporeal suturing task in a laparoscopic simulator. We describe a system providing real-time velocity and acceleration assessment in the performance of this task. Analysis of the deconstructed task segments allows targeted assessment and teaching. METHODS: A traditional box trainer was fitted with a custom-built motion-tracking system. Participants were stratified into novice, intermediate and expert groups. They performed a defined intra-corporeal suturing task. Real-time data were collected in four degrees of freedom (DOFs) (Roll, Surge, Pitch, Yaw). The task was then deconstructed into four segments: loading needle/pull-through, double-throw knot, first single-throw knot, and second single-throw knot. Motion analysis parameters (MAPs) were studied for each DOF. RESULTS: Sixty-four participants were tested (14 novices, 19 intermediates, 31 experts). The largest difference in MAPs was seen in the 'double-throw knot' segment. MAPs for the 'loading needle/pull-through' segment revealed differences between novices and experts in Roll and Pitch DOFs only. For the 'first single knot' segment, similar MAP trends were noted across all DOFs, with significant differences between novices versus experts and intermediates versus experts. For the 'second single knot' segment, the difference in MAPs was preserved only for novices versus experts. CONCLUSIONS: By analyzing motion for a defined suturing task in a laparoscopic simulator, we can gain insight into the specific hand motions distinguishing experts from non-experts. Such information may allow teaching in a more focused, effective and efficient manner.
Authors: Genevieve Dulan; Robert V Rege; Deborah C Hogg; Kristine M Gilberg-Fisher; Nabeel A Arain; Seifu T Tesfay; Daniel J Scott Journal: Surgery Date: 2012-09 Impact factor: 3.982
Authors: Iman Ghaderi; Farouq Manji; Yoon Soo Park; Dorthea Juul; Michael Ott; Ilene Harris; Timothy M Farrell Journal: Ann Surg Date: 2015-02 Impact factor: 12.969