Development of a performance model for virtual reality tumor resections

Abstract

OBJECTIVE Previous work from the authors has shown that hand ergonomics plays an important role in surgical psychomotor performance during virtual reality brain tumor resections. In the current study they propose a hypothetical model that integrates the human and task factors at play during simulated brain tumor resections to better understand the hand ergonomics needed for optimal safety and efficiency. They hypothesize that 1) experts (neurosurgeons), compared to novices (residents and medical students), spend a greater proportion of their time in direct contact with critical tumor areas; 2) hand ergonomic conditions (most favorable to unfavorable) prompt participants to adapt in order to optimize tumor resection; and 3) hand ergonomic adaptation is acquired with increasing expertise. METHODS In an earlier study, experts (neurosurgeons) and novices (residents and medical students) were instructed to resect simulated brain tumors on the NeuroVR (formerly NeuroTouch) virtual reality neurosurgical simulation platform. For the present study, the simulated tumors were divided into four quadrants (Q1 to Q4) to assess hand ergonomics at various levels of difficulty. The spatial distribution of time expended, force applied, and tumor volume removed was analyzed for each participant group (total of 22 participants). RESULTS Neurosurgeons spent a significantly greater percentage of their time in direct contact with critical tumor areas. Under the favorable hand ergonomic conditions of Q1 and Q3, neurosurgeons and senior residents spent significantly more time in Q1 than in Q3. Although forces applied in these quadrants were similar, neurosurgeons, having spent more time in Q1, removed significantly more tumor in Q1 than in Q3. In a comparison of the most favorable (Q2) to unfavorable (Q4) hand ergonomic conditions, neurosurgeons adapted the forces applied in each quadrant to resect similar tumor volumes. Differences between Q2 and Q4 were emphasized in measures of force applied per second, tumor volume removed per second, and tumor volume removed per unit of force applied. In contrast, the hand ergonomics of medical students did not vary across quadrants, indicating the existence of an "adaptive capacity" in neurosurgeons. CONCLUSIONS The study results confirm the experts' (neurosurgeons) greater capacity to adapt their hand ergonomics during simulated neurosurgical tasks. The proposed hypothetical model integrates the study findings with various human and task factors that highlight the importance of learning in the acquisition of hand ergonomic adaptation.

The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

“…[39] However these systems do not allow for an assessment of quantitative instrument force application and instrument movements in a 3D operative environment nor provide verbal, visual and auditory feedback which may enhance the learning opportunities for the trainee. [10,21,40]…”

“…[8] Virtual reality surgical simulators generate large amounts of data from each individual's specific operative performance. [5,10,11] This data can be analysed and distilled to quantitate performance and provide automated feedback to the operator. This not only provides an efficient way to understand expertise, but it can also uncover unique features of skill that may have gone previously unrecognized.…”

A systematic review of virtual reality for the assessment of technical skills in neurosurgery

“…11 Subsequent works compared force pyramids by handedness and hand ergonomics, as well as excess applied force, to detect microsurgical performance changes after sleep interruption. [38][39][40] Maximum force applied during brain tumor resection on NeuroVR was 1 of 4 APMs identified by an artificial intelligence (AI) system to predict participants' training levels. 25 Three studies examined haptic feedback perception, which is similar to force measurement.…”

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

“…This data has been utilized to create force pyramids and force heat maps to assess psychomotor function, automaticity, and force fingerprints for virtual reality tumor resections. 9,17,18,20 Force-based features extracted in this study comprise force derivatives, integral of the force, the range of the applied forces and the interquartile range, i.e., the first quartile subtracted from the third quartile. In addition to the above mentioned force-based features, parametric features including temporal and spatial features were also extracted from the force signal and its derivatives.…”

“…The simulator recorded signals including tool tip coordinates, tool tip orientation angles, contact force between virtual tool and virtual tissue and foot pedal activation state versus time. Although these signals provide useful information, previous investigations on developing a model for psychomotor performance for virtual reality tumor resections have outlined complex interacting human and task factors involved in differentiating skilled and novice performance 20. Different parametric features could be extracted from these and other derived signal features with the goal to differentiate the skilled and novice groups.…”