Evaluating the Efficacy of a Novel Multimaterial 3D-Printed Phantom for Otologic Surgical Education and Simulation
Year: 2026
Type: article
Abstract: OBJECTIVE: To design and validate a cost-effective, multimaterial 3D-printed temporal bone phantom that replicates the anatomic and haptic properties of human bone for surgical simulation and training. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary academic medical center. PARTICIPANTS: Sixteen otolaryngology residents (PGY-1 to PGY-5) are participating in a multi-institutional temporal bone dissection course. INTERVENTION: Participants performed mastoidectomy and facial recess dissection on a novel 3D-printed phantom fabricated from calcium-enriched SimuBone PLA (for osseous structures) and contrasting colored PLA (for neurovascular anatomy). Performance was compared against cadaveric controls. MAIN OUTCOME MEASURES: Participants completed a structured 5-point Likert-scale survey assessing anatomic realism, tactile feedback, visual fidelity, and educational utility. Spearman rank-order correlations evaluated the relationship between surgeon experience and perceived model fidelity. RESULTS: The phantom demonstrated moderately high scores for anatomic fidelity (mean 3.56±0.51) and visual realism (mean 3.62±0.50), with overall realism rated at 3.50±0.52. Although tactile realism was rated lower (mean 3.06±0.57), 100% of participants rated the model as moderately to highly valuable for surgical training. Senior residents were more critical of anatomic fidelity ( P <0.05) yet reported continued willingness to use the phantom for skill development. Qualitative feedback confirmed the utility of color-coded "no-go" zones for novice spatial orientation. CONCLUSIONS: The multimaterial 3D-printed phantom offers a scalable, cost-effective ($6.13 USD) alternative to cadaveric dissection with high anatomic accuracy. Although haptic nuances require further optimization, the model serves as an effective foundational simulator for otologic surgical education.
Source: Otology & Neurotology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161980216
