Ledwos Nicole, Mirchi Nykan, Yilmaz Recai, Winkler-Schwartz Alexander, Sawni Anika, Fazlollahi Ali M, Bissonnette Vincent, Bajunaid Khalid, Sabbagh Abdulrahman J, Del Maestro Rolando F
1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University.
3Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
J Neurosurg. 2022 Feb 4;137(4):1160-1171. doi: 10.3171/2021.12.JNS211563. Print 2022 Oct 1.
Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups.
A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students. All participants performed 5 repetitions of a subpial tumor resection on the NeuroVR (CAE Healthcare) platform, and 6 a priori-derived metrics selected using the K-nearest neighbors machine learning algorithm were used to assess participant learning curves. Group learning curves were plotted over the 5 trials for each metric. A mixed, repeated-measures ANOVA was performed between the first and fifth trial. For significant interactions (p < 0.05), post hoc Tukey's HSD analysis was conducted to determine the location of the significance.
Overall, 5 of the 6 metrics assessed had a significant interaction (p < 0.05). The 4 groups, neurosurgeons, seniors, juniors, and medical students, showed an improvement between the first and fifth trial on at least one of the 6 metrics evaluated.
Learning curves generated using AI-derived metrics provided novel insights into technical skill acquisition, based on expertise level, during repeated VR-simulated subpial tumor resections, which will allow educators to develop more focused formative educational paradigms for neurosurgical trainees.
了解特定外科手术中专家和实习生学习曲线的变化，对于实施形成性学习模式以加速掌握技能至关重要。本研究的目的是使用人工智能（AI）得出的指标来确定4组不同专业水平的参与者在执行一系列相同的虚拟现实（VR）软膜下切除术任务时的学习曲线，并识别这4组之间的学习曲线差异。
共有50人参与，其中14名神经外科医生、4名神经外科住院医师、10名高年资住院医师（高年资组）、10名低年资住院医师（低年资组）和12名医学生。所有参与者在NeuroVR（CAE Healthcare）平台上对软膜下肿瘤切除术进行5次重复操作，并使用通过K近邻机器学习算法选择的6个先验得出的指标来评估参与者的学习曲线。针对每个指标，在5次试验中绘制各组的学习曲线。在第一次和第五次试验之间进行混合重复测量方差分析。对于有显著交互作用（p<0.05）的情况，进行事后Tukey's HSD分析以确定显著性位置。
总体而言，评估的6个指标中有5个具有显著交互作用（p<0.05）。神经外科医生、高年资组、低年资组和医学生这4组在评估的6个指标中至少有一个在第一次和第五次试验之间表现出改善。
在重复的VR模拟软膜下肿瘤切除术中，使用AI得出的指标生成的学习曲线基于专业水平为技术技能的获取提供了新的见解，这将使教育工作者能够为神经外科实习生开发更有针对性的形成性教育模式。
