Force-based learning curve tracking in fundamental laparoscopic skills training

Abstract

BackgroundWithin minimally invasive surgery (MIS), structural implementation of courses and structured assessment of skills are challenged by availability of trainers, time, and money. We aimed to establish and validate an objective measurement tool for preclinical skills acquisition in a basic laparoscopic at-home training program.MethodsA mobile laparoscopic simulator was equipped with a state-of-the-art force, motion, and time tracking system (ForceSense, MediShield B.V., Delft, the Netherlands). These performance parameters respectively representing tissue manipulation and instrument handling were continuously tracked during every trial. Proficiency levels were set by clinical experts for six different training tasks. Resident’s acquisition and development of fundamental skills were evaluated by comparing pre- and post-course assessment measurements and OSATS forms. A questionnaire was distributed to determine face and content validity.ResultsOut of 1842 captured attempts by novices, 1594 successful trials were evaluated. A decrease in maximum exerted absolute force was shown in comparison of four training tasks (p ≤ 0.023). Three of the six comparisons also showed lower mean forces during tissue manipulation (p ≤ 0.024). Lower instrument handling outcomes (i.e., time and motion parameters) were observed in five tasks (resp. (p ≤ 0.019) and (p ≤ 0.025)). Simultaneously, all OSATS scores increased (p ≤ 0.028). Proficiency levels for all tasks can be reached in 2 weeks of at home training.ConclusionsMonitoring force, motion, and time parameters during training showed to be effective in determining acquisition and development of basic laparoscopic tissue manipulation and instrument handling skills. Therefore, we were able to gain insight into the amount of training needed to reach certain levels of competence. Skills improved after sufficient amount of training at home. Questionnaire outcomes indicated that skills and self-confidence improved and that this training should therefore be part of the regular residency training program.

The value of force and torque measurements in transanal total mesorectal excision (TaTME)

“…However, a power analysis suggests that, respectively, 90, 27 and 27 participants are needed to show significant differences (i.e., Power of 1-beta = 0.8) between the Task time, Fz max and Fft Max parameters of the pre-and post-training group. Differently from previous learning effect studies on interaction force-related parameters [40,41], we found a clear indication that the force levels are naturally decreasing during training without the use of force feedback. Tracking the learning curves of the used force and torque parameters should give insight on how subjects shift their focus on task time (i.e.…”

“…Within our pilot study, a reduction in task time is not observed, and the average task time in the novice post measurements even seem to increase. In contrast to this, the natural gradient of force parameter learning curves is more of a straight line without specific force-feedback and, therefore, indicates a limited decrease of tissue manipulation skills [40,41]. However, the averaged force parameters outcomes in this study do show a significant decrease in force parameters between pre-and post-course assessment.…”

Assessing practical laparoscopic training in certified Training Centers of the Gynecological Endoscopy Working Group (AGE) of the German Society of Gynecology and Obstetrics (DGGG)

“…Technically these advanced objective assessments can be done by sensor equipped box trainer, such as the ForceSense system (Medishield) or VR Trainer, such as the LapSim (Surgical Science) [12, 13]. However, the AGE TC clearly favor the sensor equipped box trainer over VR trainer.…”

Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography

“…Hand force/torque estimation is essential for translating human intention into control commands to external devices for various applications including human-machine interaction (Merletti et al, 2004 ; Haddadin et al, 2008 ; Pervez and Ryu, 2008 ), laparoscopic surgery training (Hardon et al, 2018 ), prosthetic control (Nielsen et al, 2010 ), tele-operation (Khurshid et al, 2016 ) and tele-assessments of home-based rehabilitation (Zhang et al, 2016 ). For human-robot interaction, it is crucial for a machine to detect an unexpected increase of contact force when the operator tries to push the machine away to protect herself/himself from injury in any hazardous situations (Haddadin et al, 2008 ; Pervez and Ryu, 2008 ).…”