Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.

ABSTRACT:

Background

Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics.

Methods

This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance.

Results

Comprehensive evaluation on both public and clinical datasets showcases the sup

SUBMITTER: Chang Q 

PROVIDER: S-EPMC11792313 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature