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

NCT ID: NCT07111481

Last Updated: 2025-12-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

40 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-08-05

Study Completion Date

2026-01-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Surgical Education

Keywords

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Neurosurgery Virtual Reality Surgical Simulation Music Mozart Effect Artificial Intelligence Intelligent Tutoring Systems

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Parallel Assignment
Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Participants
Study participants are blinded to group assignments and study outcomes.

Study Groups

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No music group

20 participants allocated. Participants perform a simulated subpial brain tumor resection on the NeuroVR simulator without music while receiving verbal feedback from the ICEMS tutor.

Group Type NO_INTERVENTION

No interventions assigned to this group

Mozart music group

20 participants allocated. Participants will listen to Mozart music while performing a simulated subpial brain tumor resection on the NeuroVR simulator and receiving verbal feedback from the ICEMS tutor.

Group Type EXPERIMENTAL

Mozart music

Intervention Type BEHAVIORAL

Participants will be played Mozart's Sonata for Two Pianos in D Major, K. 448 during their surgical simulation training session with an AI tutor.

Interventions

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Mozart music

Participants will be played Mozart's Sonata for Two Pianos in D Major, K. 448 during their surgical simulation training session with an AI tutor.

Intervention Type BEHAVIORAL

Eligibility Criteria

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Exclusion Criteria

* Prior use of the NeuroVR (CAE Healthcare) simulator.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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McGill University

OTHER

Sponsor Role lead

Responsible Party

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Rolando Del Maestro

Director, Neurosurgical Simulation and Artificial Intelligence Learning Centre

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Rolando F. Del Maestro, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Neurosurgical Simulation and Artificial Intelligence Learning Centre

Locations

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Neurosurgical Simulation and Artificial Intelligence Learning Centre

Montreal, Quebec, Canada

Site Status RECRUITING

Countries

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Canada

Central Contacts

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Bianca Giglio, MSc

Role: CONTACT

Phone: 514-802-1608

Email: [email protected]

Facility Contacts

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Rolando F. Del Maestro, MD, PhD

Role: primary

References

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Duffy MC, Lajoie SP, Pekrun R, Lachapelle K. Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Learn Instr. 2020;70:101150. doi:10.1016/j.learninstruc.2018.07.001

Reference Type BACKGROUND

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. https://www.R-project.org/

Reference Type BACKGROUND

Leppink J, Paas F, Van der Vleuten CP, Van Gog T, Van Merrienboer JJ. Development of an instrument for measuring different types of cognitive load. Behav Res Methods. 2013 Dec;45(4):1058-72. doi: 10.3758/s13428-013-0334-1.

Reference Type BACKGROUND
PMID: 23572251 (View on PubMed)

Winkler-Schwartz A, Bissonnette V, Mirchi N, Ponnudurai N, Yilmaz R, Ledwos N, Siyar S, Azarnoush H, Karlik B, Del Maestro RF. Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation. J Surg Educ. 2019 Nov-Dec;76(6):1681-1690. doi: 10.1016/j.jsurg.2019.05.015. Epub 2019 Jun 13.

Reference Type BACKGROUND
PMID: 31202633 (View on PubMed)

Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e537-e548. doi: 10.1016/S2589-7500(20)30218-1. Epub 2020 Sep 9.

Reference Type BACKGROUND
PMID: 33328048 (View on PubMed)

Wiseman MC. The Mozart effect on task performance in a laparoscopic surgical simulator. Surg Innov. 2013 Oct;20(5):444-53. doi: 10.1177/1553350612462482. Epub 2012 Nov 14.

Reference Type BACKGROUND
PMID: 23154636 (View on PubMed)

Nees LK, Grozinger P, Orthmann N, Rippinger N, Hennigs A, Sohn C, Domschke C, Wallwiener M, Rom J, Riedel F. The Influence of Different Genres of Music on the Performance of Medical Students on Standardized Laparoscopic Exercises. J Surg Educ. 2021 Sep-Oct;78(5):1709-1716. doi: 10.1016/j.jsurg.2021.03.008. Epub 2021 Mar 31.

Reference Type BACKGROUND
PMID: 33812805 (View on PubMed)

Rauscher FH, Shaw GL, Ky KN. Listening to Mozart enhances spatial-temporal reasoning: towards a neurophysiological basis. Neurosci Lett. 1995 Feb 6;185(1):44-7. doi: 10.1016/0304-3940(94)11221-4.

Reference Type BACKGROUND
PMID: 7731551 (View on PubMed)

Yilmaz R, Winkler-Schwartz A, Mirchi N, Reich A, Christie S, Tran DH, Ledwos N, Fazlollahi AM, Santaguida C, Sabbagh AJ, Bajunaid K, Del Maestro R. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. NPJ Digit Med. 2022 Apr 26;5(1):54. doi: 10.1038/s41746-022-00596-8.

Reference Type BACKGROUND
PMID: 35473961 (View on PubMed)

Other Identifiers

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2010-270, NEU-09-042-Trial 6

Identifier Type: -

Identifier Source: org_study_id