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 superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms. Conclusions: This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.