ArticleInternational dental journal2026
Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimsThis 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.
methodsCone-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.
resultsThe 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.