Laparoscopic Skills Acquisition Using an AI-enhanced Game-based Simulation Tool Compared With a Laparoscopic Simulator Box Trainer (CAMELs-RCT)

Creating New Models of Laparoscopic Surgery Skills Acquisition and Assessment: Randomised-controlled Trial of Laparoscopic Skills Acquisition Using an AI-enhanced Game-based Simulation Tool Compared With a Laparoscopic Simulator Box Trainer

The goal of this clinical trial is to learn whether an AI-enhanced, game-based laparoscopic simulation tool can improve laparoscopic skills training and help increase surgical capacity in surgical trainees and other healthcare professionals learning laparoscopic surgery. The main questions it aims to answer are:

• Does an AI-enhanced game-based simulator lead to faster and/or higher quality acquisition of laparoscopic technical skills than a standard box trainer?
• Is AI-enhanced game-based simulation a feasible and scalable model for laparoscopic skills training across diverse healthcare settings?
• Researchers will compare training with the Laptitude AI-enhanced game-based simulator to training with a standard laparoscopic box trainer to see if the AI-enhanced approach results in better performance on validated laparoscopic skills assessments and more efficient training.

Participants will:

• Be randomly assigned to train using either the Laptitude AI-enhanced game-based simulator or a standard box trainer.
• Complete a structured programme of laparoscopic training tasks.
• Undergo standardized assessments of laparoscopic skills performance during and/or after the training period.

Study Overview

• Status: Active, not recruiting
• Conditions: Laparoscopic Surgery
• Intervention / Treatment: Device: AI-enhanced game-based simulation tool (Laptitude); Device: Laparoscopic simulator box trainer (SurgTrac)

Detailed Description

Surgery is an indispensable part of healthcare but it is lacking resources. It is estimated that an additional 143 million additional surgical operations are needed each year and that 1.5 million deaths would be prevented if these operations were available. Over the next decade, the lack of surgery is projected to cost $10 billion in lost global gross domestic product (GDP).

There is an urgent need to train surgeons as this expertise is scarce. However, experience is unpredictable, skills remain unquantified, trainees require supervision, and surgeons undergo long periods of training following medical school. Surgical training which is based on graduated responsibility, defined as the progressive accumulation of skill by surgical residents that allows for the granting of greater involvement and independence by senior surgeons, forms much of the groundwork for surgical residency training.

Laparoscopic techniques have transformed surgery, being associated with less pain, lower infection rates and shorter length of stay. Of the 143 million additional operations required to meet basic needs and save lives, 28 million (20%) could be done using minimally invasive techniques. Surgeons need comprehensive training in this area through skill-based models and measurable assessment of skill acquisition to effectively track and understand the development of competencies. There is an added interest in understanding if non-MDs (non-medical doctors), undergoing similar training in laparoscopic procedures can reproduce the skills of conventionally trained surgeons.

Simulator training is a skills-based model for developing effective laparoscopic surgical skills, but existing simulators may not accurately represent real life conditions. There is a need to identify and develop high-fidelity simulators which can substitute for operative time in skill-acquisition.

The creation and deployment of complex interventions like laparoscopic skills training is challenging. Technological innovations are often complex in themselves, and they necessitate expertise and backing from a broad group of stakeholders. Furthermore, these interventions must be sufficiently flexible to meet needs across a diverse range of healthcare settings. The successful delivery of health technology programs, including laparoscopic skills training, necessitates the strong and early engagement of patients, practitioners, and policy makers. This shifts the focus from a binary question of effectiveness, to whether interventions can be acceptable, implementable, cost-effective, scalable, and transportable across settings.

This study will therefore evaluate approaches to increase surgical capacity based on the creation of new models for laparoscopic surgical skills acquisition, including this randomised controlled trial comparing of an AI-enhanced game-based simulation tool (Laptitude, Grendel Games) compared with a standard laparoscopic simulator box trainer.

• Study Type: Interventional
• Enrollment (Actual): 157
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Guatemala
  • Guatemala City, Guatemala
    • Hospital General San Juan de Dios
• India
  • Punjab
    • Ludhiana, Punjab, India, 141003
      • Christian Medical College (CMC), Ludhiana
• Nigeria
  • Ile-Ife, Nigeria
    • Obafemi Awolowo University (OAU)

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Novice medical students: no previous experience previous simulation training and/or operative experience.
• Novice medically-qualified doctors (MDs): no previous experience previous simulation training and/or operative experience.
• Intermediate experience medical students: previously used laparoscopic simulator box trainer.
• Intermediate experience medically-qualified doctors (MDs): previously used laparoscopic simulator box trainer.

Exclusion Criteria:

• Inability to provide informed consent.
• Inability to attend for training and assessment.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-enhanced game-based simulation tool; Laptitude tool.	Device: AI-enhanced game-based simulation tool (Laptitude); A surgical simulation game-based platform comprising controllers and software delivered via a laptop computer
Active Comparator: Laparoscopic simulator box trainer; eoSim tool.	Device: Laparoscopic simulator box trainer (SurgTrac); eoSim SurgTrac box trainer platform

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Composite measure of flow, handling and respect across five laparoscopic simulator tasks	A proportional odds mixed model of the domains: flow, handling and respect, measured on a 7-level Likert scale for five tasks (peg transfer, suture with extracorproeal knot, suture with intracorporeal knot, precision cutting, and ligating loop) specifying an interaction between arm and task and including participant and rater as random effects.	Final assessment is conducted after a total of 4 hours training time

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity analyais: Composite measure of flow, handling and respect across five laparoscopic simulator tasks	Sensitivity analysis: A linear mixed model of the domains: flow, handling and respect, measured on a 7-level Likert scale for five tasks (peg transfer, suture with extracorproeal knot, suture with intracorporeal knot, precision cutting, and ligating loop) specifying an interaction between arm and task and including participant and rater as random effects.	Final assessment is conducted after a total of 4 hours training time
Flow measure across five laparoscopic simulator tasks	Flow domain measured on a 7-level Likert scale for five tasks (peg transfer, suture with extracorproeal knot, suture with intracorporeal knot, precision cutting, and ligating loop) specifying an interaction between arm and task and including participant and rater as random effects.	Final assessment is conducted after a total of 4 hours training time
Handling measure across five laparoscopic simulator tasks	Handling domain measured on a 7-level Likert scale for five tasks (peg transfer, suture with extracorproeal knot, suture with intracorporeal knot, precision cutting, and ligating loop) specifying an interaction between arm and task and including participant and rater as random effects.	Final assessment is conducted after a total of 4 hours training time
Respect measure across five laparoscopic simulator tasks	Respect domain measured on a 7-level Likert scale for five tasks (peg transfer, suture with extracorproeal knot, suture with intracorporeal knot, precision cutting, and ligating loop) specifying an interaction between arm and task and including participant and rater as random effects.	Final assessment is conducted after a total of 4 hours training time
Qualitative questionnaire and interview	At the end of the assessment, all participants will be invited to complete a qualitative questionnaire gathering their feedback on the use of the technology they were randomised to. A small subset of participants will be invited to take part in a qualitative research focus group.	Final assessment is conducted after a total of 4 hours training time

Collaborators and Investigators

• Sponsor: University of Edinburgh
• Investigators: Study Chair: Ewen M Harrison, PhD, University of Edinburgh

Study record dates

Study Major Dates

• Study Start (Actual): November 19, 2025
• Primary Completion (Actual): January 23, 2026
• Study Completion (Estimated): July 31, 2026

Study Registration Dates

• First Submitted: November 18, 2025
• First Submitted That Met QC Criteria: December 8, 2025
• First Posted (Actual): December 10, 2025

Study Record Updates

• Last Update Posted (Actual): March 4, 2026
• Last Update Submitted That Met QC Criteria: March 2, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; surgery; simulation; artificial intelligence; training; global health; serious gaming; skills acquisition
• Other Study ID Numbers: 24-EMREC-003

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Full anonymised individual participant data will be available for sharing with bona fide research teams upon application to the Principal Investigator.
• IPD Sharing Time Frame: Data will be made available 6 months after completion of the study and will be available indefinitely.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No