Department of Neurology & Neurosurgery, Neurosurgical Simulation & Artificial Intelligence Learning Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Quebec, Canada.
PLoS One. 2020 Feb 27;15(2):e0229596. doi: 10.1371/journal.pone.0229596. eCollection 2020.
Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms' decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4-6 residents) and 22 novice participants (10 PGY 1-3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.
基于模拟的培训越来越多地用于评估和培训医学相关的心理运动技能。人工智能和机器学习技术的应用为利用大量数据进行教育提供了新的方法。人工智能在教育中的一个重要批评是缺乏算法决策过程的透明度。本研究旨在：1）引入一种新的基于可解释人工智能的框架，用于手术模拟培训；2）通过创建虚拟手术助手，即自动化教育反馈平台，验证该框架。28 名熟练参与者（14 名神经外科医生，4 名研究员，10 名 PGY4-6 住院医师）和 22 名新手参与者（10 名 PGY1-3 住院医师，12 名医学生）参加了这项研究。参与者在NeuroVR 模拟器上使用模拟超声吸引器和双极进行虚拟现实皮层下脑瘤切除术任务。制定了绩效指标，并采用留一法交叉验证在 Matlab 中训练和验证支持向量机。该分类器与独特的教育系统相结合，构建了虚拟手术助手，该助手根据专家绩效基准，为用户提供其绩效指标的自动化反馈。虚拟手术助手使用 4 个指标成功地对熟练和新手参与者进行分类，准确率、特异性和敏感性分别为 92%、82%和 100%。开发了一个两步反馈系统，为参与者提供与其专家绩效基准相关的即时视觉表现。所概述的教育系统为将人工智能和虚拟现实模拟整合到手术教育教学中奠定了基础。将专业知识分类、基于熟练基准的客观反馈和教师输入联系起来的潜力，通过将这三个组件整合到形成性教育模式中，创造了一个新的教育工具。
J Surg Educ. 2020
J Bone Joint Surg Am. 2019-12-4
Med Sci (Basel). 2025-8-12
J Adv Med Educ Prof. 2025-7-1
Unfallchirurgie (Heidelb). 2025-4-3
Oper Neurosurg (Hagerstown). 2020-7-1
J Bone Joint Surg Am. 2019-12-4
Nat Med. 2019-1-7
J Neurosurg. 2019-7-1
Zotero 插件
