Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.

Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.

Winkler-Schwartz A ，Bissonnette V ，Mirchi N ，Ponnudurai N ，Yilmaz R ，Ledwos N ，Siyar S ，Azarnoush H ，Karlik B ，Del Maestro RF ... -  《-》

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation.

Winkler-Schwartz A ，Yilmaz R ，Mirchi N ，Bissonnette V ，Ledwos N ，Siyar S ，Azarnoush H ，Karlik B ，Del Maestro R ... -  《JAMA Network Open》

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

Mirchi N ，Bissonnette V ，Yilmaz R ，Ledwos N ，Winkler-Schwartz A ，Del Maestro RF ... -  《PLoS One》

Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure.

Virtual reality surgical simulators are a safe and efficient technology for the assessment and training of surgical skills. Simulators allow trainees to improve specific surgical techniques in risk-free environments. Recently, machine learning has been coupled to simulators to classify performance. However, most studies fail to extract meaningful observations behind the classifications and the impact of specific surgical metrics on the performance. One benefit from integrating machine learning algorithms, such as Artificial Neural Networks, to simulators is the ability to extract novel insights into the composites of the surgical performance that differentiate levels of expertise. This study aims to demonstrate the benefits of artificial neural network algorithms in assessing and analyzing virtual surgical performances. This study applies the algorithm on a virtual reality simulated annulus incision task during an anterior cervical discectomy and fusion scenario. An artificial neural network algorithm was developed and integrated. Participants performed the simulated surgical procedure on the Sim-Ortho simulator. Data extracted from the annulus incision task were extracted to generate 157 surgical performance metrics that spanned three categories (motion, safety, and efficiency). Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Center, McGill University, Montreal, Canada. Twenty-three participants were recruited and divided into 3 groups: 11 post-residents, 5 senior and 7 junior residents. An artificial neural network model was trained on nine selected surgical metrics, spanning all three categories and achieved 80% testing accuracy. This study outlines the benefits of integrating artificial neural networks to virtual reality surgical simulators in understanding composites of expertise performance.

Alkadri S ，Ledwos N ，Mirchi N ，Reich A ，Yilmaz R ，Driscoll M ，Del Maestro RF ... -  《-》

VR and machine learning: novel pathways in surgical hands-on training.

Veneziano D ，Cacciamani G ，Rivas JG ，Marino N ，Somani BK ... -  《-》