3D palatal soft tissue analysis

: a pilot study

Abstract

Background: Periodontal diseases, including gingivitis and periodontitis, significantly affect oral health and are influenced by various factors. Traditional methods of diagnosis and treatment, limit the effectiveness of interventions. The adoption of advanced digital technologies, like intraoral scanners, 3D imaging (CBCT) and AI, has transformed periodontology, enabling more accurate diagnoses, precise planning, and effective disease management. A detailed understanding of periodontal anatomy, especially of the palatal mucosa and neurovascular bundle position, is crucial for successful surgery. Integrating these technologies with sound anatomical knowledge fosters the development of innovative therapies, improving clinical outcomes. Materials and methods: CBCT bone models (STL files) and 3D impression models (PLY files) of the upper jaw were prepared for 49 patients, 51% of whom were women. The study measured the palatal mucosal thickness (PMT) in the palatal region (ROI 1-4), analyzed parameters of the palatal artery (PA), palatal depth (PD), great palatal foramen (GPF) and the palatal angle (Pang). The models were loaded into coDiagnostiX® and Geomagic® software for the evaluation. Patients were identified with clinical process numbers to maintain anonymity. Results: The study showed that men had a higher average PMT than women in several measurements. A weak but significant correlation was found between age and certain mucosal thickness parameters, while no significant correlation emerged between age and palatal artery position. Differences in PD between sexes were found to be statistically significant. Conclusions: This study revealed that male subjects had significantly higher mean values in PMT than females, with greater palatal depth and maximum size. Palatal angle was stable between the sexes, while GPF position remained constant. There were no significant differences in PA variables between sexes and age groups. These results underscore the importance of considering sexes and age to improve the accuracy of dental treatment.

Date of Award	19 Jul 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Tiago Marques (Supervisor), Danilo Fernandes (Co-Supervisor) & Filipe Miguel Araújo (Co-Supervisor)