Evaluation of a 3D printed training model with realistic spatial-anatomical conditions for head and neck microsurgery.

Abstract

Objectives: Although existing microsurgical models provide a high degree of realism in tissue properties, they often neglect the complex and constrained spatial-anatomical conditions typical of head and neck surgery. This study aims to evaluate the effectiveness of the Head and Neck Realistic Anatomical Condition Experience (RACE) model in enhancing microsurgical education.

Methods: Using a microsurgical competency assessment tool and self-assessment questionnaires, the head and neck RACE model was evaluated through application in two student courses (10 participants) and one resident course (5 participants). In both groups, first the conventional chicken thigh model and then the RACE model were applied. Data were analyzed using a two-way repeated measures ANOVA with Welch's statistics to assess differences between the groups.

Results: In pregraduate courses, the transition from the conventional chicken thigh model to the RACE model initially led to a decline across all eight microsurgical performance parameters (Q1.1-Q4.2). However, after an additional day of training with the RACE model, all parameters-except tissue-preserving technique (Q1.2) - returned to or significantly exceeded baseline levels (Q1.2 p = 0.373, Q1.3 p = 0.003, Q2.1 p < 0.001, Q2.2 p = 0.022, Q2.3 p = 0.008, Q3.1 = 0.014, Q4.1 p = 0.036, Q4.2 p = 0.002). Conversely, residents showed immediate improvement in all parameters, except for suture distance to the vessel's margin, upon switching to the RACE model.

Conclusions: Head and neck RACE models provide a challenging and practical addition to microsurgery teaching.

Clinical relevance: The positive impact on learning outcomes in this area supports the development of RACE models in other areas of microsurgical and general medical training, and therefore the education of students and clinical practitioners.