A High-Fidelity Neurosurgical Training Platform for Bimanual Procedures: A Feasibility Study

TL;DR

This study addresses the challenge of objectively training and evaluating bimanual neurosurgical skills by developing a high-fidelity ex vivo calf-brain platform integrated with multi-camera instrument tracking. Through three simulated subpial corticectomy trials across 47 participants of varying expertise, the authors extract motion-based, time-based, and efficiency/coordination metrics using data processed in 3D Slicer and OpenIGTLink. While motion-based metrics were not consistently discriminative, several time-based and bimanual coordination metrics distinguished lower- from higher-expertise groups, demonstrating the platform’s potential for objective performance assessment. The findings support the platform’s feasibility and point to its value as a foundation for future machine-learning driven tutoring and an Intelligent Operating Room framework to enhance neurosurgical training and reduce adverse outcomes.

Abstract

Background. Bimanual psychomotor proficiency is fundamental to neurosurgical procedures, yet it remains difficult for trainees to acquire and for educators to objectively evaluate performance. In this study, we investigate the feasibility of a neurosurgical simulation platform that integrates an anatomically realistic brain model with surgical instrument tracking to support training and objective assessment of bimanual tasks in the context of subpial corticectomy. Methods. We developed and evaluated a neurosurgical simulation platform based on an ex-vivo calf brain model and a multi-camera tracking system capable of simultaneously capturing the motion of surgical instruments in both hands, including collection of real-time instrument trajectories and synchronized video recordings. These enabled extraction of motion-based, time-based, and bimanual coordination metrics. We conducted a case series involving 47 participants across four training levels: medical students, junior residents, senior residents, and neurosurgeons. Results. The tracking system successfully captured instrument motion during 81% of the periods when instruments were actively used throughout the simulation procedure. Several extracted metrics were able to significantly differentiate between levels of surgical expertise. In particular, instrument usage duration and custom-defined bimanual coordination metrics such as instrument tip separation distance and simultaneous usage time, show potential as features to identify participant expertise levels with different instruments. Conclusions. We demonstrated the feasibility of tracking surgical instruments during complex bimanual tasks in an ex-vivo brain simulation platform. The metrics developed provide a foundation for objective performance assessment and highlight the potential of motion analysis to improve neurosurgical training and evaluation.

Figures (9)

• Figure 1: Overview of the simulation platform: (a) surgical (red), position tracking (yellow), and video recording (blue) components, (b) tracked instruments, and (c) setup showing the skull and craniotomy before installation of the calf brain.
• Figure 2: Coordinate system hierarchy and transform relationships in the simulation platform.
• Figure 3: Motion-based metric results for velocity, acceleration and jerk.
• Figure 4: The Normalized Path Length denotes the distance traveled by the instrument divided by its usage time: Annotated normalized path length was measured using Usage Time ($T_\textit{Usage}$), and Captured normalized path length was measured using Captured Time ($T_\textit{Capt}$).
• Figure 5: Single instrument time results: Usage Time ($T_\textit{Usage}$) is derived from manual video annotations, Tracking Time ($T_\textit{Track}$) denotes successful system tracking, and Captured Time ($T_\textit{Capt}$) denotes effective tracking within the manually annotated window.