Journal of Tissue Engineering and Reconstructive Surgery ›› 2024, Vol. 20 ›› Issue (6): 605-.

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Automated cephalometric analysis of craniomaxillofacial deformities based on artificial intelligence technologies

  

  • Online:2024-12-02 Published:2025-01-03

Abstract:

Objective To develop a new automatic landmark detection framework for the diagnosis and treatment of patients with severe craniomaxillofacial (CMF) deformities, addressing the issues of limited data quantity and large morphological differences. Methods This study proposed a method based on a three-dimensional (3D) point cloud deformation model and deep learning networks. First, normal human data was deformed to simulate severe CMF patient data for data augmentation. Then, a coarse-to-fine strategy was adopted, where initial coarse localization of landmarks was performed using a 3D point cloud convolutional neural network (CNN) semantic segmentation model, followed by fine localization using different models based on whether the landmarks are located in bone defect areas. Results The experiments demonstrated that the proposed method outperformed existing technologies in the detection of both normal and defective landmarks. The average errors for normal landmarks and defective landmarks detected under CT scanning were 1.19 mm and 1.13 mm, respectively, and under CBCT scanning were 0.91 mm and 0.94 mm, respectively. Conclusion The new method can effectively improve the accuracy of landmark detection for severe CMF deformities, which is significant for clinical surgical design and patient treatment.

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