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Purpose <p>Tooth segmentation and diagnosis of dental crowding severity on 3D intraoral scan models are key processes for computer-aided analysis of orthodontic models. Conventional methods are time-consuming, inefficient, and subjective, necessitating more efficient and intelligent approaches. Therefore, we propose a two-stage intelligent workflow.</p> Methods <p>In Stage 1, tooth segmentation is performed using an innovative dual-dilated graph convolutional network 1 (DDGCNet1). In Stage 2, Stage 1’s output is converted to a point cloud, then processed by DDGCNet2 and post-processing to generate arch length discrepancy (ALD, an indicator of dental crowding). The encoding layers of the proposed networks embed a novel dual-dilated EdgeConv module, effectively learning from local features and long-range contextual information of adjacent teeth.</p> Results <p>Experimental comparative analysis demonstrates that the proposed network achieves outstanding segmentation performance and accurate dental crowding diagnosis. In ALD measurement, it attains a mean absolute error (MAE) of 1.553&#xa0;mm for the maxilla and 1.434&#xa0;mm for the mandible.</p> Conclusion <p>This study can assist orthodontists in diagnosis and treatment, alleviate their workload, and expedite the development of reliable orthodontic treatment plans, thereby meeting the demands of computer-aided orthodontic diagnosis.</p>

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Tooth segmentation and dental crowding diagnosis using two-stage dual-dilated graph convolution

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Zongsong Han,
  • Ning Dai,
  • Zhilei Wu,
  • Bin Yan,
  • Luwei Liu,
  • Bingting Shao

摘要

Purpose

Tooth segmentation and diagnosis of dental crowding severity on 3D intraoral scan models are key processes for computer-aided analysis of orthodontic models. Conventional methods are time-consuming, inefficient, and subjective, necessitating more efficient and intelligent approaches. Therefore, we propose a two-stage intelligent workflow.

Methods

In Stage 1, tooth segmentation is performed using an innovative dual-dilated graph convolutional network 1 (DDGCNet1). In Stage 2, Stage 1’s output is converted to a point cloud, then processed by DDGCNet2 and post-processing to generate arch length discrepancy (ALD, an indicator of dental crowding). The encoding layers of the proposed networks embed a novel dual-dilated EdgeConv module, effectively learning from local features and long-range contextual information of adjacent teeth.

Results

Experimental comparative analysis demonstrates that the proposed network achieves outstanding segmentation performance and accurate dental crowding diagnosis. In ALD measurement, it attains a mean absolute error (MAE) of 1.553 mm for the maxilla and 1.434 mm for the mandible.

Conclusion

This study can assist orthodontists in diagnosis and treatment, alleviate their workload, and expedite the development of reliable orthodontic treatment plans, thereby meeting the demands of computer-aided orthodontic diagnosis.