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

Kexin Yang; Wenjun Duan; Wangtao Lu; Zheyuan Sun; Rongan Li; Jiakang Yang; Baixiang Wang

doi:10.1016/j.identj.2026.109468

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

Int Dent J. 2026 Mar 6;76(3):109468. doi: 10.1016/j.identj.2026.109468. Online ahead of print.

Authors

Kexin Yang¹, Wenjun Duan¹, Wangtao Lu², Zheyuan Sun¹, Rongan Li¹, Jiakang Yang³, Baixiang Wang⁴

Affiliations

¹ Stomatology 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.
² State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China.
³ Stomatology 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. Electronic address: JackYoung@zju.edu.cn.
⁴ Stomatology 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. Electronic address: wangbaixiang@zju.edu.cn.

PMID: 41795243
DOI: 10.1016/j.identj.2026.109468

Abstract

This study aimed to evaluate the performance of deep learning models for segmenting the augmented bone following transalveolar sinus floor elevation (TSFE). Cone-beam computed tomography (CBCT) data from 103 patients undergoing TSFE, acquired at preoperative (T0) and immediate postoperative (T1) were retrospectively analysed. Four deep learning models (UNETR++, Swin Transformer, U-Net, 3D-VNet) were trained and validated for segmenting the augmented bone. Performance was assessed using the Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, precision, 95% Hausdorff Distance (HD95), and accuracy. UNETR++ demonstrated the best performance, with an average DSC of 0.8477, IoU of 0.7356, sensitivity of 0.8337, precision of 0.8622, HD95 of 0.9234 mm, and accuracy of 0.8730. UNETR++ segmentations exhibited excellent reproducibility compared with manual segmentation. The automated segmentation process significantly reduced measurement time to 14.96 ± 2.57 seconds. Deep learning models, particularly UNETR++, provide an accurate and efficient method for segmenting augmented bone after TSFE.

Keywords: Artificial intelligence; Automatic segmentation; Bone augmentation; Deep learning; TSFE; UNETR++.