Objectives: Accurate assessment of postoperative bone graft material changes after the 1-stage sinus lift is crucial for evaluating long-term implant survival. However, traditional manual labeling and segmentation of cone-beam computed tomography (CBCT) images are often inaccurate and inefficient. This study aims to utilize artificial intelligence for automated segmentation of graft material in 1-stage sinus lift procedures to enhance accuracy and efficiency.
Materials and methods: Swin-UPerNet along with mainstream medical segmentation models, such as FCN, U-Net, DeepLabV3, SegFormer, and UPerNet, were trained using a dataset of 120 CBCT scans. The models were tested on 30 CBCT scans to evaluate model performance based on metrics including the 95% Hausdorff distance, Intersection over Union (IoU), and Dice similarity coefficient. Additionally, processing times were also compared between automated segmentation and manual methods.
Results: Swin-UPerNet outperformed other models in accuracy, achieving an accuracy rate of 0.84 and mean precision and IoU values of 0.8574 and 0.7373, respectively (p < 0.05). The time required for uploading and visualizing segmentation results with Swin-UPerNet significantly decreased to 19.28 s from the average manual segmentation times of 1390 s (p < 0.001).
Conclusions: Swin-UPerNet exhibited high accuracy and efficiency in identifying and segmenting the three-dimensional volume of bone graft material, indicating significant potential for evaluating the stability of bone graft material.
Keywords: 1‐stage sinus lift; Swin‐UPerNet; artificial intelligence; automatic segmentation; bone augmentation; deep learning.
© 2024 Wiley Periodicals LLC.