Effect of Music on Surgical Performance During Artificial Intelligence-Based Simulation Training

Overview

At the Neurosurgical Simulation and Artificial Intelligence Learning Centre, we seek to provide surgical trainees with innovative technologies that allow them to improve their surgical technical skills in risk-free environments, potentially improving patient operative outcomes. The Intelligent Continuous Expertise Monitoring System (ICEMS), a deep learning application that assesses and trains neurosurgical technical skill and provides continuous intraoperative feedback, is one such technology that may improve surgical education. Previous research has found that music can impact cognitive performance and learning outcomes. However, the effects of music on neurosurgical simulation performance-along with the associated affective-cognitive responses-remain largely unexplored. In this randomized controlled trial, medical students from four Quebec universities will be blinded and randomized to one of two groups. The control group will undergo a simulation training session without music, while the intervention arm will listen to a Mozart piano sonata during their session. The aim of this study is to determine how listening to Mozart music during surgical simulation training influences learner technical skill acquisition and transfer, as well as their emotions and cognitive load.

Background: The Intelligent Continuous Expertise Monitoring System (ICEMS) is a deep learning application that was developed at the Neurosurgical Simulation and Artificial Intelligence Learning Centre to improve neurosurgical education. The ICEMS assesses and trains bimanual surgical performance by providing continuous feedback via verbal instructions in order to improve trainee performance and mitigate errors. At present, how learners respond to music during surgical training with the ICEMS is unknown. Rationale: The Mozart effect refers to the short-term enhancement in spatial-temporal reasoning that occurs in learners when they listen to Mozart music. Previous studies have found that exposure to Mozart and/or classical music before or during surgical simulation training can lead to improved performance. However, these studies did not involve a structured artificial intelligence (AI)-enhanced curriculum or objective, quantitative performance assessment based on AI-derived metrics. Moreover, these studies have not assessed how exposure to music during surgical simulation training influences learners' emotions and cognitive load. This report follows the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) as well as the Machine Learning to Assess Surgical Expertise (MLASE) checklist. Hypotheses: 1. Listening to a Mozart piano sonata during surgical simulation training will result in superior skill acquisition and transfer in trainees compared with no music. 2. Listening to a Mozart piano sonata during surgical simulation training will result in lower levels of negative emotions and cognitive load compared with no music. Primary Objectives: To determine how listening to Mozart music during surgical simulation training influences trainee: 1. Trainee technical skill acquisition and overall surgical performance (composite expertise scores across all practice tasks calculated by the ICEMS). 2. Trainee skill transfer to a more complex realistic scenario (composite expertise scores during realistic task calculated by the ICEMS). Secondary Objective: To determine how listening to Mozart music during surgical simulation training influences trainee affective-cognitive responses, including emotions-self-reported via questionnaires administered before, during, and after each training session using the Medical Emotions Scale (MES) on 7-point Likert scales-and cognitive load-self-reported via questionnaire administered after each training session using the Cognitive Load Index (CLI) on 5-point Likert scales. Setting: McGill University's Neurosurgical Simulation and Artificial Intelligence Learning Centre. Participants: Students enrolled in their preparatory, first, or second year at one of four Quebec medical schools. Design: A single-blinded two-arm randomized crossover trial. Intervention: Participants will undergo two separate training sessions of approximately 90 minutes each on the NeuroVR (CAE Healthcare), a virtual reality (VR) surgical simulator that simulates a subpial brain tumor resection. In this study, participants will perform two different scenarios on the NeuroVR: a simple practice scenario and a complex realistic scenario. During each session, participants will perform four repetitions of the practice scenario (5 minutes each) followed by the realistic scenario (13 minutes). The ICEMS will continuously assess performance throughout the trials. Group 1 (control) will complete their training session without music. Group 2 will listen to Mozart's Sonata for Two Pianos in D Major, K. 448 during their training session. Verbal feedback will be based on the following six metrics: 1. Tissue injury risk: When a trainee receives feedback on this metric, the healthy brain tissue has been damaged or is at risk of being damaged. 2. Bleeding risk: When a trainee receives feedback on this metric, there is bleeding that must be cauterized or a risk of bleeding. 3. Instrument tip separation distance: Refers to the distance between the tip of the ultrasonic aspirator and the tips of the bipolar forceps. When a trainee receives feedback on this metric, their instruments are too far apart. 4. High bipolar force: Refers to the amount of force applied to the tissue by the bipolar forceps. When a trainee receives feedback on this metric, they are applying too much force with the bipolar. 5. Low bipolar force: Refers to the amount of force applied to the tissue by the bipolar forceps. When a trainee receives feedback on this metric, they are not applying enough force with the bipolar. 6. High aspirator force: Refers to the amount of force applied to the tissue by the ultrasonic aspirator. When a trainee receives feedback on this metric, they are applying too much force with the aspirator. These metrics will continuously be evaluated by the ICEMS. The ICEMS will only provide feedback on one metric at a time according to a predetermined hierarchy (in the order listed above). For example, if the ICEMS detects on error on both bleeding risk (2) and high aspirator force (6) at the same time, the system will only provide feedback on bleeding risk since this metric is above high aspirator force in the hierarchy. Study Procedure: Prior to the simulation session, the study coordinator will stratify participants according to their year in medical school and block randomize them to one of three intervention arms with an allocation ratio of 1:1. Upon arrival, participants will read and sign an informed consent form. They will then fill out a pre-trial questionnaire recording demographic information and self-reported baseline emotions using the MES. Trial instructions introducing the NeuroVR simulator, the instruments, and the practice subpial resection scenario will be provided via a written document. Each practice task will last 5 minutes, followed by a 5-minute rest period. No post-hoc feedback will be provided during the rest periods. Participants will perform their first practice task without feedback to establish a baseline. Participants will then perform their second through fourth practice tasks while receiving metric-specific verbal feedback from the ICEMS. During these formative practice tasks, music will be turned on for the experimental group and paused during the rest periods. Following the completion of the practice tasks, participants will complete a peri-trial questionnaire to assess their emotions using the MES. They will be provided with another information document introducing the realistic subpial brain tumor resection task. Participants will complete the 13-minute realistic task to assess their skill transfer to a more complex scenario. Finally, they will fill out a post-trial questionnaire assessing their emotions using the MES and their cognitive load using the CLI.