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.
Yang et al. (Fri,) studied this question.