Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study.

ABSTRACT:

Background

Gingival phenotype plays an important role in dental diagnosis and treatment planning. Traditionally, determining the gingival phenotype is done by manual probing of the gingival soft tissues, an invasive and time-consuming procedure. This study aims to evaluate the feasibility and accuracy of an alternatively novel, non-invasive technology based on the precise 3-dimension (3D) soft tissue reconstruction from intraoral scanning and cone beam computed tomography (CBCT) to predict the gingival biotype.

Methods

As a proof-of-concept, Yorkshire pig mandibles were scanned, and the CBCT data were fed into a deep-learning model to reconstruct the teeth and surrounding bone structure in 3D. By overlaying the CBCT scan with the intraoral scans, an accurate superposition wa

SUBMITTER: Yang M 

PROVIDER: S-EPMC10149545 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature