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Development and validation of an artificial intelligence-powered 3D digital twin for teaching and assessing
Giulia Di Nardo1,2,3, Chang John Tan4,5, Ryan Sadeghian-Nia4,5
1Surgical Artificial Intelligence Research Academy, University Health Network, 399 Bathurst St., Toronto, ON, M5T 2S8, Canada. giulia.dinardo@utoronto.ca.
Background:
Developing professional expertise warrants deliberate practice, where skills are developed and refined through repeated engagement with targeted tasks. Deliberate practice is difficult to implement and rarely available in the field of surgery. Digital twins, which are virtual replications of anatomy and behavior, show promise in facilitating deliberate practice and addressing the limitations presented by augmented and virtual reality systems. In this work, we investigated CholeCoach, a scalable surgical education platform for laparoscopic cholecystectomy in teaching retraction through comparison between trainee and expert surgeons and gaining their end-user experience.
Methods:
Digital twin simulations were generated using a machine learning pipeline from six laparoscopic cholecystectomy videos. Surgical trainees and experts were asked to virtually retract the gallbladder to expose target areas of dissection (15 tasks, 5 cases). Retraction vector data were collected and compared between groups. A post-study 5-point Likert scale survey was used to evaluate the educational value, usability, and design of the platform.
Results:
Eighteen participants (11 trainees, 7 experts) engaged with the CholeCoach platform. On average, trainees pulled with 11% [95% CI 4-19%, p = 0.035] less force, had 1.8 [95% CI 1.5-2.2, p < 0.001] times higher within-task variability in force, and covered 31% [95% CI 16-48%, p = 0.004] larger gallbladder surface area than experts. Qualitative assessment of the platform through surveys was positive. Most participants found the platform to be a useful adjunct to current surgical training, with potential to improve trainee performance. Participants suggested additional functionalities and automated comparison with expert retraction.
Conclusion:
This study revealed significant discrepancy between expert and trainee approaches to retraction, objectively demonstrating the potential of CholeCoach in enabling deliberate practice and automated assessment. Qualitative assessment was positive and will be used to inform further development of CholeCoach. Digital twin-based simulation has potential to facilitate surgical training and democratize high-quality surgical education across healthcare institutions.