# Fabrication and evaluation of a novel patient-specific 3D-printed simulation model for oral surgical training

## TL;DR

This paper introduces a new method for creating accurate 3D-printed models of patients' mouths to train oral surgeons, showing they are effective for students and could be improved for experts.

## Contribution

A novel workflow for fabricating patient-specific 3D-printed models that replicate both hard and soft tissues for oral surgical training.

## Key findings

- The printed models showed high dimensional accuracy when compared to patient scans.
- Students rated the model's educational value higher than experienced periodontists.
- Periodontists suggested improvements in soft-tissue realism to enhance training effectiveness.

## Abstract

Use of patient-specific models as a surgical planning and training tool can support novice practitioners’ surgical skill development. This study aimed to introduce a novel workflow for fabricating 3D-printed, patient-specific simulation models and evaluate their accuracy and transferability for use in periodontal and oral surgery training.

Patient-specific anatomical models of the maxilla were fabricated using the CBCT and intraoral scan data. The proposed workflow outlines a novel process for creating a patient-specific model that accurately replicates both the hard and soft tissues of the patient. The accuracy of the printed models was evaluated by scanning five models and comparing them to the patient’s intraoral scan using cloud-to-cloud distance analysis. Then, in an exploratory study design, a simulated gingival flap surgery exercise was completed by 18 periodontists and 50 students. The face and content validity of the model were assessed using an 8-item online questionnaire with a VAS of 0-100 and a free comment question. The data were analyzed using descriptive statistics, Mann–Whitney U tests and independent t-test.

The printed model demonstrated high dimensional accuracy. The overall VAS score of the model was significantly higher for students than for periodontists (83.7 ± 9.7 vs. 72.1 ± 15.8, p < 0.006). The face and content validity scores reported by students were also higher (P < 0.01), with mean differences of 8.86% and 12.62%, respectively. Periodontists rated the models lower for soft-tissue tactile feedback, particularly during incision.

The proposed 3D-printed simulation workflow produced an accurate and educationally valuable model with the potential to enhance surgical training. Experienced surgeons suggested that refining the soft-tissue realism could further improve its overall educational value.