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

ABSTRACT: 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 signi

SUBMITTER: Qiu T 

PROVIDER: S-EPMC12779949 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature