3D-Printed Anatomical Models for Surgical Training: Design, Implementation, Evaluation

Thesis Abstract

The integration of 3D-printed anatomical models into surgical training addresses persistent gaps in hands-on experience for complex procedures, enabling standardized, repeatable practice opportunities in a risk-free environment. Despite advances in digital fabrication, there remains limited empirical evidence on how design features of printed models influence psychomotor performance, spatial understanding, and overall educational outcomes across surgical specialties. This study aims to evaluate the design, implementation, and effectiveness of 3D-printed anatomical models as a training intervention for residents and medical students, with specific objectives to (1) develop a modular library of high-fidelity, patient-specific and generic anatomical replicas across thoracic, abdominal, and craniofacial surgical domains; (2) implement a structured training curriculum incorporating deliberate practice, feedback, and assessment aligned with Keller’s ARCS motivational model; (3) quantify improvements in technical skill and decision-making using objective structured assessment of technical skill (OSATS) scores and procedure-specific checklists; (4) examine knowledge gains and transfer to cadaveric or simulated operative performance; and (5) evaluate learner perceptions of realism, usability, and educational value through qualitative feedback. The study adopts a mixed-methods design anchored in situated learning theory and cognitive load theory to explore how tangible models mediate experiential learning, pattern recognition, and procedural sequencing. A multi-site sample of 120 participants, including 60 surgical residents at postgraduate level and 60 medical students in clinical rotations, will be recruited using stratified random sampling to ensure representation across training levels and specialties. Quantitative data will be collected via pre- and post-training OSATS assessments (12 stations), procedurally focused checklists for 6 representative simulations, a knowledge test with 40 multiple-choice items, and time-to-task completion metrics. Reliability of instruments will be established through Cronbach’s alpha (? ? 0.80) for checklists and inter-rater agreement (Cohen’s ? ? 0.70) for OSATS scoring, with blinded assessors. Qualitative data will be gathered through semi-structured interviews (n=24) and focus groups (n=6 sessions) to capture perceived realism, cognitive ease or load, and perceived translation to actual operative performance, analyzed thematically in line with Braun and Clarke’s approach. Data analysis will employ (i) repeated-measures ANOVA to compare pre- and post-training OSATS and knowledge test scores by group and specialty, with post-hoc Tukey tests to identify specific differences; (ii) multilevel linear modeling to account for clustering by training site; (iii) regression analysis to identify predictors of skill improvement, including prior experience and model realism ratings; and (iv) thematic analysis for qualitative data, triangulated with quantitative results to provide a comprehensive interpretation. The expected findings include statistically significant enhancements in technical skills (mean OSATS improvement ? 15 points on a 100-point scale), improved procedural confidence, and meaningful transfer effects to cadaveric simulations in at least 70% of participants, with qualitative themes highlighting realism, haptic feedback, and structured feedback as critical facilitators. The study anticipates that modular, anatomically accurate 3D-printed models will yield superior transfer of knowledge and skills compared with traditional didactic approaches, supporting the theory that tangible, closed-loop practice accelerates skill acquisition and retention. The contribution to knowledge encompasses (a) a validated design framework for producing cost-effective, scalable 3D-printed models with reproducible fabrication parameters and material considerations; (b) an evidence base for incorporating 3D-printed simulators into surgical curricula, including optimal session structure and assessment timing; and (c) empirical insights into how realism, fidelity, and feedback interact to influence learning outcomes in anatomy-based surgical training. The study concludes that 3D-printed anatomical models, when integrated within a deliberate-practice framework and coupled with structured assessment and feedback, substantially improve psychomotor skills, spatial cognition, and procedural confidence, with positive attitudes toward simulation-based training. Recommendations include standardizing model specifications and validation protocols, scaling the model library to additional procedures, integrating simulator-based training early in residency programs, and exploring cost-benefit analyses to inform institutional adoption.