Related Experiment Video
Updated: Jun 27, 2026
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning
Jiajin Shen1, Xi Yang2, Junbiao Zhang3
1College of Stomatology, Zunyi Medical University, Zunyi, Guizhou, China; Guiyang Stomatology Hospital, Guiyang, Guizhou, China.
Introduction And Aims:
This study aimed to devise a deep learning-based model for the automated identification of anatomical mandibular lingual concavities in the posterior mandible and the prediction of biologically guided three-dimensional implant positions.
Methods:
Cone-beam computed tomography (CBCT) images with single-tooth posterior mandibular edentulism were included. A deep learning framework was constructed utilizing 3D U-Net and Swin-Transformer. This framework was designed to perform automated segmentation of teeth, mandible, and the mandibular nerve canal; to classify morphological types of lingual concavities; and to identify implant key points with coordinate prediction. Model performance was assessed via five-fold cross-validation.
Results:
The proposed model achieved Dice similarity coefficients ranging from 0.87 to 0.91 for dental segmentation. In the classification of mandibular lingual concavities, an accuracy of 0.92 to 0.97 was attained. Regarding the prediction of three-dimensional implant positions, the automatically generated plans maintained a safety margin of 3.20 to 3.89 mm from the mandibular nerve canal. Furthermore, sufficient bone volume was preserved at both cervical and apical implant levels, with buccal/lingual cervical bone widths averaging 4.94 to 5.78 mm.
Conclusion:
The deep learning model in this experiment performed well across different views and learning tasks in a retrospective internal validation setting. Furthermore, it demonstrated the ability to accurately identify relevant anatomical structures, and the predicted three-dimensional implant positions showed clinically acceptable safety margins in the internal validation cohort.
Clinical Relevance:
This model provides a preliminary AI-assisted framework for identifying critical mandibular anatomical structures and generating biologically guided preliminary implant position suggestions, thereby helping to mitigate intraoperative complications and reduce the risk of postoperative mechanical and biological complications in preclinical evaluation.
