Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.

ABSTRACT:

Background

Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an early stage and motivate patients themselves to visit the dentist is required.

Objective

This study aimed to examine the feasibility of building an AI model that can detect malocclusion in children during the mixed dentition stage.

Methods

This study was conducted as a feasibility study using cross-sectional data. Subjects were recruited from panelists registered with Macromill, Inc. (approximately 1.3 million registered in 2021). A total of 519 elementary school children (275 boys and 244 girls in Grades 3-6) were included i

SUBMITTER: Oka K 

PROVIDER: S-EPMC12688100 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature