Evaluation of Deep Learning for Caries Detection With Fine-Grained Classification and Postprocessing Improvements.

ABSTRACT:

Objectives

Deep learning methods have been proven to be effective in detecting dental caries in visible light images. However, existing research involves inadequate categories and mainly focuses on local lesion areas. This study aims to use advanced deep learning models to achieve caries detection based on tooth instances (where all teeth in images are detected) and fine-grained classification according to the International Caries Detection and Assessment System (ICDAS). To address the potential instability under complex scenarios, we propose 2 correction methods that incorporate background knowledge.

Methods

A total of 1200 selected high-quality intraoral images were expanded to 8,754 images using data augmentation techniques, and each tooth inside was annotated. Three adva

SUBMITTER: Yang L 

PROVIDER: S-EPMC12305711 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature