A comprehensive artificial intelligence framework for dental diagnosis and charting.

ABSTRACT:

Background

The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses.

Methods

The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patie

SUBMITTER: Kabir T 

PROVIDER: S-EPMC9647924 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature