Surgical Training Simulators for Rhinoplasty: A Systematic Review.

Rhinoplasty training currently follows an apprenticeship model that is largely observational. Trainees have limited experience in performing maneuvers of this complex surgery. Rhinoplasty simulators can address this issue by providing trainees with the opportunity to gain surgical simulator experience that could improve technical competences in the operating room. This review amalgamates the collective understanding of rhinoplasty simulators described to date. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, PubMed, OVID Embase, OVID Medline, and Web of Science databases were all searched for original research on surgical simulators for rhinoplasty education and reviewed by independent reviewers. Articles underwent title and abstract screening, and then relevant articles underwent full-text review to extract simulator data. Seventeen studies, published between 1984 and 2021, were included for final analysis. Study participant numbers ranged from 4 to 24, and included staff surgeons, fellows, residents (postgraduate year 1-6), and medical students. Cadaveric surgical simulators comprised eight studies, of which three were with human cadavers, one study was a live animal simulator, two were virtual simulators, and six were three-dimensional (3D) models. Both animal and human-based simulators increased the confidence of trainees significantly. Significant improvement in various aspects of rhinoplasty knowledge occurred with implementation of a 3D-printed model in rhinoplasty education. Rhinoplasty simulators are limited by a lack of an automated method of evaluation and a large reliance on feedback from experienced rhinoplasty surgeons. Rhinoplasty simulators have the potential to provide trainees with the opportunity for hands-on training to improve skill and develop competencies without putting patients in harm's way. Current literature on rhinoplasty simulators largely focuses on simulator development, with few simulators being validated and assessed for utility. For wider implementation and acceptance, further refinement of simulators, validation, and assessment of outcomes is required.