发表机构
The Affiliated Stomatological Hospital of Nanjing Medical University; Nanjing Medical University; Jiangsu Province Engineering Research Center of Stomatological Translational Medicine; The Chinese University of Hong Kong（南京医科大学附属口腔医院; 南京医科大学; 江苏省口腔转化医学工程研究中心; 香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对错颌畸形骨骼分级依赖人工测量的问题，提出TeethGNN框架，融合CBCT图像特征与形态信息，结合协作校准策略，实现自动分级，准确率达77.08%，AUC达89.61%。
AI 中文摘要
错颌畸形骨骼分级是正畸学中的一项基础任务，对诊断和治疗计划至关重要。传统上，锥形束计算机断层扫描（CBCT）用于视觉测量，重建的侧位头影测量片交由专家牙医进行诊断。然而，人工审查耗时费力，且存在操作者间差异。因此，需要一种基于CBCT的自动系统来实现可靠的错颌畸形骨骼分级。在此情况下，我们开发了TeethGNN，一种新颖的基于图的框架，旨在将CBCT图像特征与形态信息相结合，以实现准确高效的错颌畸形分级。TeethGNN利用一个解耦的可学习解码器直接从CBCT图像预测关键形态指标，无需人工测量。然后，这些形态特征通过图神经网络（GNN）与图像特征融合，有效建模模态间的关系。为进一步增强鲁棒性和校准，我们引入了一种协作校准策略。该策略结合了多尺度图对抗扰动进行显式校准，以及非线性拓扑图校准进行隐式置信度调整。在我们收集的临床数据集上进行的大量实验和消融研究表明，我们的错颌畸形测量系统在准确率上达到77.08%，AUC达到89.61%，优于所比较的最先进方法。这些结果验证了基于图的多模态融合和协作校准在提高错颌畸形分级性能方面的有效性。我们的系统在推进计算机辅助正畸诊断方面显示出巨大潜力，为基于视觉的临床测量和诊断提供了准确可靠的解决方案。
英文摘要
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.
CommentsAccepted by Biocybernetics and Biomedical Engineering
