QDentalNet: a geometry-aware hierarchical mesh learning and quantum-enhanced network for automated dental occlusion classification

The accurate analysis of dental occlusion is important to orthodontic diagnosis and occlusal classification. The existing assessment procedures largely depend on clinician-driven evaluation of intraoral scans, which introduces subjectivity and limits scalability in routine diagnostic workflows. To address these challenges, this study proposes an automated dental occlusion classification architecture that integrates geometry-aware mesh learning with a quantum-enhanced network.

The proposed QDentalNet employs a hierarchical mesh-processing backbone designed to capture both local and global geometric characteristics of dental structures. Rotation-aware feature encoding and kernel-based geometric correlation are incorporated through the Face Rotation Convolution (FRC) and Face Kernel Correlation (FKC) modules. The network also adopts a learning strategy with task towers and quantum-enhanced classification heads. This design helps the prediction of multiple clinically relevant occlusal classes. Additionally, auxiliary features via upper and lower contact point estimation enable efficient feature learning. The study also presents a novel composite loss function that enhances the discriminative capability of the network by addressing class imbalance, anatomical consistency, and efficient feature learning.

The proposed network achieved an average classification accuracy of 80.46% across all occlusal tasks. The obtained performance was also contextualized against previously reported results from existing 3D dental classification approaches as reference benchmarks.

To the best of our knowledge, this is one of the initial studies to incorporate geometry-aware features with quantum-based decision-making for dental occlusion classification, providing a proof-of-concept for automated occlusal assessment.