Bridging the Gap in Rhinoplasty Training: The Effectiveness of 3D Printed Models in Surgical Education
Year: 2025
Type: article
Abstract: BACKGROUND: Rhinoplasty is a common facial plastic surgery procedure for both functional and aesthetic indications. The use of 3-dimensional (3D) models has been reported as a potential method for providing hands-on training for learning rhinoplasty without jeopardizing patient care. OBJECTIVES: The objective of this study was to develop and validate a novel model for rhinoplasty training. METHODS: The rhinoplasty models were designed and produced with proprietary 3D printing technology. Face and content validity were assessed during a rhinoplasty course involving 53 surgeons. Criterion validity was evaluated in a training session with 20 surgical residents, measuring improvements in surgical skills after practice with the 3D models, with an objective structured assessment of technical skills (OSAT). RESULTS: All surgeons (n = 53, 100%) stated that the rhinoplasty model aided in their learning and development. In all, 91% (n = 48) of surgeons rated the realism of the model as excellent or very good in comparison to cadaveric specimens. Assessment of criterion validity showed a statistically significant improvement in OSAT scores among surgical residents, increasing from a baseline of 11.7/40 (SD ± 1.80) to 21.6/40 (SD ± 1.79) post session (P < .0001). CONCLUSIONS: The 3D rhinoplasty models showed good content, face, and criterion validity, objectively improving residents' surgical performance. Rhinoplasty 3D models may serve as a precadaveric training adjunct, equipping trainees with fundamental skills before cadaveric dissection, or as a primary training modality in countries with limited cadaver access. Therefore the models offer an innovative approach to training the next generation of rhinoplasty surgeons.
Source: Aesthetic Surgery Journal
DOI: 10.1093/asj/sjaf045
Authors: Umar Rehman, Natasha Polglase, David Kahn, Teoman Dogan, Santdeep Paun, Alwyn D’Souza, Rajan Uppal, Nicholas Eynon-Lewis, Matt Lechner
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,601
OpenAlex ID: W4408845432
