Clin Oral Implants Res. 2023 Mar 12. doi: 10.1111/clr.14063. Online ahead of print.
OBJECTIVES: To develop and assess the performance of a novel artificial intelligence (AI)-driven convolutional neural network (CNN)-based tool for automated three-dimensional (3D) maxillary alveolar bone segmentation on cone-beam computed tomography (CBCT) images.
MATERIAL AND METHODS: A total of 141 CBCT scans were collected for performing training (n=99), validation (n=12) and testing (n=30) of the CNN model for automated segmentation of the maxillary alveolar bone and its crestal contour. Following automated segmentation, the 3D models with under- or over-estimated segmentations were refined by an expert for generating a refined-AI (R-AI) segmentation. The overall performance of CNN model was assessed. Also, 30% of the testing sample was randomly selected and manually segmented to compare the accuracy of AI and manual segmentation. Additionally, the time required to generate a 3D model was recorded in seconds (s).
RESULTS: The accuracy metrics of automated segmentation showed an excellent range of values for all accuracy metrics. However, the manual method (95% HD: 0.20±0.05 mm; IoU: 95%±3.0; DSC: 97%±2.0) showed slightly better performance than the AI segmentation (95% HD: 0.27±0.03 mm; IoU: 92%±1.0; DSC: 96%±1.0). There was a statistically significant difference of the time-consumed amongst the segmentation methods (p<0.001). The AI-driven segmentation (51.5±10.9s) was 116 times faster than the manual segmentation (5973.3±623.6s). The R-AI method showed intermediate time-consumed (1666.7±588.5s).
CONCLUSION: Although the manual segmentation showed slightly better performance, the novel CNN-based tool also provided a highly accurate segmentation of the maxillary alveolar bone and its crestal contour consuming 116 times less than the manual approach.