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

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.