Graduate Student Seminar Series – Yuyang (Tony) Jiao

Graduate Student Seminar Series
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Location: MS2158 – 1 King’s College Circle
Presentation Title: Patient Specific Virtual Reality for Spine Surgery Education
Abstract:
Background & Prior Work: Effective surgical education benefits from multimodal approaches, yet hands-on training is constrained by patient safety and limited operating room access. Virtual Reality (VR) simulations improve cognitive and procedural performance, providing risk-free practice environments (1). Existing VR simulations for spine surgery focus on standardized, generic scenarios, they fail to align with the specific cases trainees encounter (2) impacting their adoption by trainees. Our novel approach generates patient-specific 3D models to enhance relevance and integration into competency-based curricula. Spinal stenosis treatment requires decompression procedures that rely on thoroughly understanding spinal anatomy (bone, ligament, disc, and neural elements), as neural compression occurs at multiple sites (i.e., central, lateral recess), and from many degenerative anatomical changes. Previous work has demonstrated VR’s effectiveness in spinal decompression training, with participants highlighting its utility for learning spinal stenosis pathoanatomy (88%), laminectomy concepts (75%), and preoperative planning (96%). Our group has also shown the effectiveness and broad acceptance of VR for spinal decompression surgery simulation. The training was shown to be successful, with junior (PGY-I and PGY-II) residents exhibiting the strongest improvement in test scores following utilization of the VR simulation module (3).
Objective & Hypothesis: This study aims to evaluate a VR training platform using patient-specific 3D anatomical models to improve orthopaedic spine surgery education through realistic and clinically relevant simulations. We predict that this approach will significantly enhance trainees’ knowledge, decision-making, and long-term competency while increasing confidence and preparedness for live surgeries, especially among junior residents.
Proposed Methods: The unique nature of our platform is the use of patient specific models. Current workflows for building these models require significant time and multiple software packages, which is impractical for routine surgical training. We have developed tools for automating this workflow, using Magnetic Resonance (MR) and Computed Tomography (CT) images to detect and segment relevant anatomical landmarks (4-7). Generated models will include detailed anatomical structures necessary for simulating spinal decompression procedures. The training will involve two modes: multi-player sessions, where staff surgeons guide trainees through procedures in a collaborative virtual operating room, and individual practice sessions, allowing trainees to refine their skills independently. To assess the effectiveness of the training, pre- and post-session tests will measure knowledge of anatomy, surgical strategy, and decision-making, with statistical analysis using Fisher’s exact test to evaluate short-term improvements (8). Long-term effects will be measured through annual assessments, correlating cumulative VR exposure tracked by session frequency and duration, with competency improvements. Learners will be drawn from the University of Toronto’s orthopaedic residency and spine fellowship programs. A sample size of 30 learners will be recruited to detect significant correlations, assuming a correlation coefficient of 0.5 (9). Usage metrics such as access duration and models loaded will be monitored through the VR platform, and low-usage participants will be contacted to address potential barriers to access.
Anticipated Results & Significance: Our novel approach to surgical education could further advance surgical education by providing risk-free, engaging, and realistic training. Our platform offers a captivating and authentic environment, which we expect will maintain trainees’ interest by allowing practice on relevant anatomy and strategies. This integration is only possible through our patient-specific modeling. The completed project will offer a VR simulation fully embedded into surgical education, improving trainees’ familiarity, preparedness, and skills for live surgeries, leading to heightened surgeon confidence in independent practice.
References: (1) M. Pfandler et al., Spine J. 17, 1352-1363 (2017); (2) A. Feeley et al., Injury. 52, 1715-1720 (2021); (3) T. Chen et al., N. Am. Spine Soc. J. 6, 100063 (2021); (4) G. Klein et al., in MICCAI, Y. Cai et al., Eds., 2020, pp. 15-28; (5) V. Sivan et al., 2023, https://arxiv.org/abs/2302.02283v1; (6) C. Caldwell et al., World Congr. Med. Phys. & Biomed. Eng., 2015, p. 2401; (7) T. Mahmood et al., World J Gastroenterol. 24, 5439 (2018); (8) J. Harrop et al., Neurosurgery. 73, 94-99 (2013); (9) M. Mahalingam, E. Fasella, Eff. Use Technol. Async. Learn., 2017.
Supervisor Name: Michael Hardisty
Year of Study: 2
Program of Study: MASc
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