Simulation models for training skin flap and graft surgery in the head region: a narrative review

The demand for skill development in the surgical field is critical to achieve the best functional and aesthetic patient results. Therefore, the use of simulation models has become necessary and integrated in training, holding substantial importance in skin reconstruction in the head. These models, while varying for realism and training applications, offer a low-stress, safe, and replicable environment for interns and residents to enhance their surgical technique.

The aim of this review was to analyze the various head simulators designed for skin flap and skin graft training. The anatomical representation, selected materials, manufacturing process, and validation approaches were examined to provide an overview of the characteristics of the models. In addition, their impact on training was assessed, categorizing the outcome as positive, negative, or with no effect, based on the findings of the reviewed studies.

Published literature on PubMed and Scopus was gathered through relevant keywords and phrases related to dermatological facial surgery simulators. A narrative synthesis was conducted based on the reporting guidelines of the synthesis without meta-analysis method. The head models were evaluated with overall performance as the primary outcome and confidence, planning and design, and execution as the secondary outcomes.

Thirteen studies on head models for skin procedures were identified between 2004 and 2023. All the simulators reviewed demonstrated variability for supported techniques, composition, manufacturing methods, anatomical detail, and validation approach. Eleven studies demonstrated that the models improved at least one of the selected outcomes. No model was targeted for skin graft reconstruction. Furthermore, none of the models integrated objective feedback mechanisms.

Simulation was proved to enhance the surgical training of dermatological reconstructions in the head, despite variations in realism, complexity and production process. Future efforts should prioritize higher anatomical accuracy, cost-efficiency, and integration of feedback mechanisms to improve the educational value of these tools.