This study evaluated the performance of a multi-stage Segmentation Residual Network
(SegResNet)-based deep learning (DL) model for the automatic segmentation of cone-beam
computed tomography (CBCT) images of patients with stage III and IV periodontitis.Seventy
pre-processed CBCT scans from patients undergoing periodontal rehabilitation were
used for training and validation. The model was tested on 10 CBCT scans independent
from the training dataset by comparing results with semi-automatic (SA) segmentations.
Segmentation accuracy was assessed using the Dice similarity coefficient (DSC), Intersection
over Union (IoU), and Hausdorff distance 95th percentile (HD95). Linear periodontal
measurements were performed on four tooth surfaces to assess the validity of the DL
segmentation in the periodontal region.The DL model achieved a mean DSC of 0.9650
± 0.0097, with an IoU of 0.9340 ± 0.0180 and HD95 of 0.4820 mm ± 0.1269 mm, showing
strong agreement with SA segmentation. Linear measurements revealed high statistical
correlations between the mesial, distal, and lingual surfaces, with intraclass correlation
coefficients (ICC) of 0.9442 (p<0.0001), 0.9232 (p<0.0001), and 0.9598(p<0.0001),
respectively, while buccal measurements revealed lower consistency, with an ICC of
0.7481 (p<0.0001). The DL method reduced the segmentation time by 47 times compared
to the SA method.Acquired 3D models may enable precise treatment planning in cases
where conventional diagnostic modalities are insufficient. However, the robustness
of the model must be increased to improve its general reliability and consistency
at the buccal aspect of the periodontal region.This study presents a DL model for
the CBCT-based segmentation of periodontal defects, demonstrating high accuracy and
a 47-fold time reduction compared to SA methods, thus improving the feasibility of
3D diagnostics for advanced periodontitis.
