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Related Experiment Video

Updated: Jul 22, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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Automated Segmentation of Augmented Bone After Transalveolar Sinus Floor Elevation Using Deep Learning.

Kexin Yang¹, Wenjun Duan¹, Wangtao Lu²

¹Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Zhejiang University School of Medicine, Cancer Center of Zhejiang University, Hangzhou, China.

International Dental Journal

|March 8, 2026

Summary

Deep learning accurately segments augmented bone after transalveolar sinus floor elevation (TSFE). The UNETR++ model showed superior performance and efficiency, significantly reducing measurement time.

Keywords:

Artificial intelligence Automatic segmentation Bone augmentation Deep learning TSFE UNETR++

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Area of Science:

Biomedical Engineering
Radiology
Artificial Intelligence in Medicine

Background:

Transalveolar sinus floor elevation (TSFE) is a common procedure to augment bone volume.
Accurate segmentation of augmented bone is crucial for evaluating treatment outcomes.
Manual segmentation is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To evaluate the performance of deep learning models for automated segmentation of augmented bone post-TSFE.
To compare the accuracy and efficiency of different deep learning architectures.

Main Methods:

Retrospective analysis of Cone-beam computed tomography (CBCT) data from 103 patients.
Training and validation of four deep learning models: UNETR++, Swin Transformer, U-Net, and 3D-VNet.
Performance evaluation using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), sensitivity, precision, Hausdorff Distance (HD95), and accuracy.

Main Results:

UNETR++ achieved the highest performance with an average DSC of 0.8477 and IoU of 0.7356.
UNETR++ demonstrated excellent reproducibility compared to manual segmentation.
Automated segmentation significantly reduced measurement time to 14.96 ± 2.57 seconds.

Conclusions:

Deep learning models, especially UNETR++, offer an accurate and efficient solution for augmented bone segmentation after TSFE.
Automated segmentation facilitates objective assessment of bone augmentation and treatment success.