| 蒋晓彤,刘小峰.错颌畸形的自动化诊断[J].电子测量与仪器学报,2025,39(4):105-113 |
| 错颌畸形的自动化诊断 |
| Automated diagnostics of malocclusion |
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| DOI： |
| 中文关键词: 错颌畸形 自动化检测 图神经网络 图卷积网络 深度学习 |
| 英文关键词:malocclusion automated detection graph neural networks graph convolutional networks deep learning |
| 基金项目:国家自然科学基金（62276090，62406102，62476079）、江苏省重点研发计划 (BZ2024061)项目资助 |
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| 中文摘要: |
| 针对目前传统的错颌畸形诊断的主观性强、效率低等问题，设计了一种基于图神经网络（GNN）的错颌畸形自动化诊断方法GraphTeeth。GraphTeeth结合了图神经网络的架构优势，能够有效捕捉牙齿及其周围结构的拓扑信息。通过将牙齿的位置、形态以及相互关系建模为图结构，利用节点间的消息传递机制学习到更加精细的局部特征和全局特征。在实验阶段，使用了包含各类错颌畸形病例的大型数据集来训练和测试GraphTeeth。实验结果显示，GraphTeeth在关键性能指标上显著优于现有的目标检测方法。在平均精确率均值（mAP）指标上，GraphTeeth达到了43.45%，相较于传统的目标检测算法如Mask R-CNN的32.26%、EfficientDet的38.73%和DETR的25.05%有显著提高。此外，对于特定类型的错颌畸形——如固定矫治器的配戴——GraphTeeth的准确率高达91.28%，而对于健康牙齿的识别率则达到了83.91%。结果表明，GraphTeeth能够提供更快、更准确、更客观的诊断，为正畸治疗提供可靠支持。 |
| 英文摘要: |
| Aiming at the problems of high subjectivity and low efficiency of the current traditional diagnosis of malocclusion, the article designs GraphTeeth, an automated diagnosis method of malocclusion based on graph neural network. GraphTeeth combines the architectural advantages of graph neural network, which is able to efficiently capture the topological information of the teeth and their surrounding structures. By modeling the position, morphology, and interrelationships of teeth as graph structures, finer local and global features are learned using a message passing mechanism between nodes. In the experimental phase, a large dataset containing various types of malocclusion cases was used to train and test GraphTeeth.The experimental results show that GraphTeeth significantly outperforms existing target detection methods in the key performance metrics. On the mAP metric, GraphTeeth achieves 43.45%, which is a significant improvement over traditional target detection algorithms such as Mask R-CNN at 32.26%, EfficientDet at 38.73%, and DETR at 25.05%. In addition, for specific types of malocclusions-such as fixed orthodontic appliance fitting-GraphTeeth achieves an accuracy of 91.28%, while the recognition rate for healthy teeth reaches 83.91%. The results suggest that GraphTeeth is able to provide faster, more accurate and objective diagnosis, providing reliable support for orthodontic treatment. |
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