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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.

International Dental Journal

|June 25, 2026

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This summary is machine-generated.

A new deep learning model accurately identifies mandibular lingual concavities and predicts implant positions using cone-beam computed tomography (CBCT) scans. This AI tool enhances safety and precision in dental implant planning.

Area of Science:

* Artificial Intelligence in Dentistry
* Medical Imaging Analysis
* Oral and Maxillofacial Surgery

Background:

* Accurate identification of anatomical landmarks in the posterior mandible is crucial for safe dental implant placement.
* Mandibular lingual concavities and the mandibular nerve canal pose significant risks if not properly identified.
* Current methods for implant planning can be time-consuming and may lack precision.

Purpose of the Study:

* To develop a deep learning model for automated identification of mandibular lingual concavities.
* To predict precise, biologically guided three-dimensional (3D) implant positions.
* To assess the model's performance in segmenting anatomical structures and classifying concavities.

Main Methods:

Keywords:

CBCT Deep learning Implant three-dimensional position Mandibular lingual concavity Preoperative planning

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* A deep learning framework using 3D U-Net and Swin-Transformer was developed.

* The model processed cone-beam computed tomography (CBCT) images of patients with posterior mandibular edentulism.

* Automated segmentation of teeth, mandible, and mandibular nerve canal, plus classification of lingual concavities and implant key point prediction were performed.

Main Results:

* High accuracy was achieved in dental segmentation (Dice coefficients 0.87-0.91) and lingual concavity classification (0.92-0.97).
* Predicted implant positions maintained a safety margin of 3.20-3.89 mm from the mandibular nerve canal.
* Sufficient bone volume was preserved, with average buccal/lingual cervical bone widths of 4.94-5.78 mm.

Conclusions:

* The deep learning model demonstrated robust performance in identifying anatomical structures and predicting implant positions.
* The model offers a clinically acceptable safety margin for implant placement in internal validation.
* This AI framework aids in mitigating intraoperative complications and reducing risks of postoperative complications.