Development of a 3D-printed skull base model for surgical anatomy education and evaluation

Thesis Abstract

The increasing demand for realistic, cost-effective, and ethically sound surgical education tools has highlighted a gap between traditional cadaveric resources and scalable training modalities for skull base procedures. This study addresses the problem of limited access to anatomically accurate, repeatable, and standardized skull base models that support both foundational anatomy learning and competency-based evaluation in surgical education. The aim is to develop, validate, and evaluate a high-fidelity 3D-printed skull base model and to integrate a structured assessment framework that quantifies educational impact. Specific objectives are (1) to design a anatomically faithful skull base model replicating key osseous landmarks, foramina, fissures, and adjacent neurovascular corridors; (2) to implement a multimaterial 3D-printing workflow that combines rigid and compliant components to simulate tissue-tactile properties; (3) to construct a comprehensive assessment battery comprising objective structured clinical examination (OSCE) stations, a knowledge test, and a practical skill checklist aligned with the Korea University anatomical education framework and the Canadian Task Force on 3D-printed models; (4) to evaluate educational effectiveness with a quasi-experimental design comparing the 3D-printed model to cadaveric specimens and traditional textbooks; (5) to examine construct validity through content, convergent, and discriminant validity analyses; and (6) to explore user experiences and perceived realism via thematic analysis of facilitator and learner interviews. The methodology adopts a mixed-methods, design-based research approach integrating principles from constructivist learning theory and Kolb’s experiential learning cycle. A purposive sample of 60 medical and surgical residents across otolaryngology, neurosurgery, and maxillofacial surgery will participate, with 30 allocated to the intervention group using the 3D-printed model and 30 to traditional teaching modalities. The data collection instruments include (i) a 3D-printed skull base model validated by an expert panel of five skull base surgeons using a Likert-scale realism rubric; (ii) a validated OSCE instrument consisting of five stations simulating cistern exploration, internal auditory canal navigation, and skull base fracture assessment; (iii) a multiple-choice knowledge assessment with 40 items on skull base anatomy and surgical approaches; (iv) a practical skill checklist with 25 task items; (v) a post-session Likert-scale satisfaction survey; and (vi) semi-structured interviews with 12 participants and 5 instructors. Validity and reliability will be established through content validity ratio (CVR) with a panel of experts, inter-rater reliability using intraclass correlation coefficients (ICC > 0.80) for OSCE scoring, and Cronbach’s alpha (>0.80) for knowledge and skill instruments. Data analysis will employ descriptive statistics and inferential methods independent-samples t-tests or Mann-Whitney U tests for group comparisons, repeated-measures ANOVA to assess learning progression across sessions, and multiple regression to identify predictors of performance. Thematic analysis will be conducted on interview transcripts using Braun and Clarke’s method, with triangulation against quantitative findings to enhance construct validity. Key expected findings include improved practical performance in skull base navigation and instrument handling for the intervention group, evidenced by higher OSCE scores (mean difference anticipated 8–12 points on a 100-point scale, p < 0.05) and superior knowledge retention at a 4-week follow-up. The model is anticipated to demonstrate superior realism ratings on the expert panel’s rubric (mean scores ?4.5/5) and favorable learner satisfaction (?4.0/5). Regression analyses are expected to reveal significant associations between tactile realism, task fidelity, and objective performance. The study also anticipates identifying specific subdomains where 3D-printed models outperform traditional resources, such as safely simulating hazardous or anatomy-dense regions (e.g., petrous apex, internal carotid canal) without ethical or logistical constraints. The study contributes to knowledge by providing a replicable, scalable design for high-fidelity skull base education that harmonizes anatomical accuracy with pragmatic assessment, advancing evidence on the educational value of 3D-printed simulators in surgical training. It informs curriculum design by offering a validated evaluation framework and a transparent methodology for integrating 3D-printed models into resident training programs. The main conclusion is that a rigorously designed 3D-printed skull base model, coupled with a structured assessment battery, can enhance anatomy mastery and procedural competence more consistently than traditional approaches, while enabling standardized, ethical, and cost-effective training. Recommendations include adopting modular printing workflows for rapid iteration, expanding the model to cover additional pathological variations, conducting longitudinal studies to assess long-term skill retention, and exploring cross-institutional replication to generalize findings.