背景:虚拟和增强现实手术模拟器,与机器学习集成,对训练精神运动技能至关重要,并分析手术性能。尽管有像连接权重算法这样的方法,这些试验典型的小样本量(参与者数量少(N))挑战了模型的普适性和稳健性.诸如数据增强和来自在类似手术任务上训练的模型的迁移学习之类的方法解决了这些限制。
目的:为了证明人工神经网络和迁移学习算法在评估虚拟手术性能方面的有效性,应用于增强和虚拟现实模拟器中的模拟斜外侧腰椎椎间融合技术。
方法:这项研究在一个新颖的模拟器平台中开发并集成了人工神经网络算法,使用来自模拟任务的数据生成276个跨运动的性能指标,安全,和效率。创新,它从为类似的脊柱模拟器开发的预训练ANN模型应用迁移学习,加强培训过程,解决小数据集的挑战。
方法:肌肉骨骼生物力学研究实验室;神经外科模拟和人工智能学习中心,麦吉尔大学,蒙特利尔,加拿大。
方法:27名参与者分为3组:9名居民后,6名高级居民和12名初级居民。
结果:两种模型,一个从头开始训练的独立模型和另一个利用迁移学习的模型,对9个选定的手术指标进行了培训,分别达到75%和87.5%的测试准确率。
结论:本研究通过策略使用迁移学习和数据增强,为解决手术模拟中的有限数据集提供了新的蓝图。它还评估并加强了我们以前出版物中连接权重算法的应用。一起,这些方法不仅提高了性能分类的准确性,而且促进了手术训练平台的验证。
BACKGROUND: Virtual and augmented reality surgical simulators, integrated with machine learning, are becoming essential for training psychomotor skills, and analyzing surgical performance. Despite the promise of methods like the Connection Weights Algorithm, the small sample sizes (small number of participants (N)) typical of these trials challenge the generalizability and robustness of models. Approaches like data augmentation and transfer learning from models trained on similar surgical tasks address these limitations.
OBJECTIVE: To demonstrate the efficacy of artificial neural network and transfer learning algorithms in evaluating virtual surgical performances, applied to a simulated oblique lateral lumbar interbody fusion technique in an augmented and virtual reality simulator.
METHODS: The study developed and integrated artificial neural network algorithms within a novel simulator platform, using data from the simulated tasks to generate 276 performance metrics across motion, safety, and efficiency. Innovatively, it applies transfer learning from a pre-trained ANN model developed for a similar spinal simulator, enhancing the training process, and addressing the challenge of small datasets.
METHODS: Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada.
METHODS: Twenty-seven participants divided into 3 groups: 9 post-residents, 6 senior and 12 junior residents.
RESULTS: Two models, a stand-alone model trained from scratch and another leveraging transfer learning, were trained on nine selected surgical metrics achieving 75 % and 87.5 % testing accuracy respectively.
CONCLUSIONS: This study presents a novel blueprint for addressing limited datasets in surgical simulations through the strategic use of transfer learning and data augmentation. It also evaluates and reinforces the application of the Connection Weights Algorithm from our previous publication. Together, these methodologies not only enhance the precision of performance classification but also advance the validation of surgical training platforms.