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<p>Accurate three-dimensional (3D) craniofacial soft tissue analysis is crucial for diagnosing malocclusion and formulating personalized orthodontic treatment plans. However, the automated localization of 3D landmarks is often hindered by complex anatomy and significant biological variability.To address this challenge, we developed an innovative two-stage attention-based deep learning framework for robust landmark detection and diagnostic classification. Our approach leverages PointTransformerV3(PTv3) as its backbone, augmented by two novel modules: a Geodesic Crop module that isolates the facial region via curvature-aware geodesic masking and a Dynamic Landmark Structure Learning module that incorporates anatomical priors to model spatial interdependencies. This integrated architecture significantly enhances localization precision and structural consistency. We evaluated performance of our approach by using three complementary metrics: mean radial error (MRE), successful detection rate (SDR) across clinically relevant thresholds (2–4&#xa0;mm), and successful classification rate(SCR) for treatment difficulty. Our framework achieved state-of-the-art results, with an MRE of 2.17 ± 1.54&#xa0;mm (test set) and 2.19 ± 1.60&#xa0;mm (validation set), alongside high SDR values meeting clinical tolerances. Notably, the model achieved a 91.74% diagnostic accuracy in classifying orthodontic treatment difficulty, underscoring its strong potential for clinical application. Comparative analyses confirmed significant improvements over existing methods in both landmark precision and diagnostic utility. Overall, these results validate the efficacy of our two-stage framework in automating craniofacial morphology assessment. By synergistically integrating geometric cropping, attention mechanisms, and anatomical constraints, the system offers orthodontists a reliable tool to enhance diagnostic precision, optimize treatment planning, and improve outcomes in patients with malocclusion.</p>

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Attention-Base deep learning for 3D craniofacial soft tissue landmark detection and diagnosis in orthodontics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月29日

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  • Tao Qiu,
  • Chaoran Hu,
  • Jingyu Zhang,
  • Fuli Wu,
  • Huiming Wang,
  • Xiangtao Liu,
  • Mouyuan Sun

摘要

Accurate three-dimensional (3D) craniofacial soft tissue analysis is crucial for diagnosing malocclusion and formulating personalized orthodontic treatment plans. However, the automated localization of 3D landmarks is often hindered by complex anatomy and significant biological variability.To address this challenge, we developed an innovative two-stage attention-based deep learning framework for robust landmark detection and diagnostic classification. Our approach leverages PointTransformerV3(PTv3) as its backbone, augmented by two novel modules: a Geodesic Crop module that isolates the facial region via curvature-aware geodesic masking and a Dynamic Landmark Structure Learning module that incorporates anatomical priors to model spatial interdependencies. This integrated architecture significantly enhances localization precision and structural consistency. We evaluated performance of our approach by using three complementary metrics: mean radial error (MRE), successful detection rate (SDR) across clinically relevant thresholds (2–4 mm), and successful classification rate(SCR) for treatment difficulty. Our framework achieved state-of-the-art results, with an MRE of 2.17 ± 1.54 mm (test set) and 2.19 ± 1.60 mm (validation set), alongside high SDR values meeting clinical tolerances. Notably, the model achieved a 91.74% diagnostic accuracy in classifying orthodontic treatment difficulty, underscoring its strong potential for clinical application. Comparative analyses confirmed significant improvements over existing methods in both landmark precision and diagnostic utility. Overall, these results validate the efficacy of our two-stage framework in automating craniofacial morphology assessment. By synergistically integrating geometric cropping, attention mechanisms, and anatomical constraints, the system offers orthodontists a reliable tool to enhance diagnostic precision, optimize treatment planning, and improve outcomes in patients with malocclusion.