Automated Classification of Enamel Caries from Intraoral Images Using Deep Learning Models: A Diagnostic Study.

ABSTRACT: Background: Dental caries is a prevalent global oral health issue. The early detection of enamel caries, the initial stage of decay, is critical to preventive dentistry but is often limited by the subjectivity and variability of conventional diagnostic methods. Objective: This study aims to develop and evaluate two explainable deep learning models for the automated classification of enamel caries from intraoral images. Dataset and Methodology: A publicly available dataset of 2000 intraoral images showing early-stage enamel caries, advanced enamel caries, no-caries was used. The dataset was split into training, validation, and test sets in a 70:15:15 ratio, and data preprocessing and augmentation were applied to the training set to balance the dataset and prevent model

SUBMITTER: Asiri FYI 

PROVIDER: S-EPMC12734310 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature