Automatic graph-based system improves skeletal malocclusion grading accuracy
Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
Computer Vision and Pattern Recognition
Summary
Malocclusion means teeth and jaws are misaligned, and checking this is important for dental care. The usual way uses 3D X-rays and expert dentists, which takes a lot of time and can vary between experts. The researchers created TeethGNN, a computer program that uses 3D X-ray images to automatically find key shapes and combine this information with image details to diagnose malocclusion more accurately. Their method works better than existing ones and can help dentists by providing faster, more reliable assessments.
What this means in practice
- •For orthodontists: Automatically assess skeletal malocclusion from CBCT scans to speed diagnosis and reduce variability between clinicians.
- •For dental imaging software developers: Integrate graph-based multimodal fusion techniques for improved automated cephalometric analysis in orthodontic planning tools.$Commercial implications: Enables development of smarter dental imaging products that provide accurate, automated malocclusion grading to orthodontic practices.
Authors
Zhichun Jin, Zhicheng He, Hao Xu, Dongyang Li, Lin Wang, Hongliang Ren, Long Bai
Abstract
Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed tomography (CBCT) is used for visual measurement, and the reconstructed lateral cephalograms are handed over to expert dentists for diagnosis. However, manual review is time-consuming, labor-intensive, and subject to inter-operator variability. Therefore, an automatic CBCT-based system is needed for reliable malocclusion skeletal grading. In this case, we develop TeethGNN, a novel graph-based framework designed to combine CBCT image features with morphological information for accurate and efficient malocclusion grading. TeethGNN utilizes a decoupled learnable decoder to directly predict key morphological indicators from CBCT images, eliminating the need for manual measurements. These morphological features are then fused with image features using a graph neural network (GNN), which effectively models the relationships between the modalities. To further enhance robustness and calibration, we introduce a collaborative calibration strategy. This strategy combines multi-scale graph adversarial perturbation for explicit calibration and nonlinear topological graph calibration for implicit confidence adjustment. Extensive experiments and ablation studies on our collected clinical dataset demonstrate that our malocclusion measurement system achieves 77.08\% in accuracy and 89.61\% in AUC, outperforming the compared state-of-the-art methods. These results validate the effectiveness of graph-based multimodal fusion and collaborative calibration in improving malocclusion grading performance. Our system shows strong potential for advancing computer-aided orthodontic diagnosis, providing an accurate and reliable solution for vision-based clinical measurement and diagnosis.