Objective
This study aims to utilize a deep learning-based automatic tooth segmentation model to assist in measuring the prevalence and severity of orthodontically induced external root resorption (OIERR), and to compare the differences in OIERR caused by treatment with fixed appliances versus clear aligners in adults.
Methods
The study included 25 patients treated with fixed appliances (FA group) and 25 with clear aligners (CA group). The Shapiro-Wilk test assessed the comparability of baseline characteristics between groups. All patients underwent pre-treatment (T0) and post-treatment (T1) CBCT scans, and images were segmented to generate 3D models of the dentition.This study employed the HMGNet enhanced with the Swin Transformer block for automatic tooth segmentation of CBCT images. 3-matic software facilitated semi-automatic alignment and calculation of root length and volume. Paired-sample t-tests analyzed changes within each group, and the Mann-Whitney U test compared OIERR between groups.
Results
The accuracy, precision, F1 score, IOU index, and Dice coefficient for automatic tooth segmentation were 99.90%, 97.62%, 96.53%, 93.28%, and 96.53%, respectively. Significant reductions in root length and volume were observed in both groups (P < 0.05). The FA group showed an average root length change of 0.80 ± 0.72 mm and root volume change of 12.57 ± 11.30 mm³, whereas the CA group had changes of 0.61 ± 0.49 mm and 11.21 ± 10.88 mm³, respectively. Inter-group comparisons indicated a root length reduction of 6.52% in the FA group and 4.84% in the CA group, and a root volume resorption rate of 4.32% in the FA group compared to 3.51% in the CA group. Differences were statistically significant (P < 0.05).
Conclusions
The study applied an automatic evaluation method for root resorption using a tooth segmentation network, providing an effective tool for monitoring root resorption. Clear aligners result in significantly less root resorption compared to fixed orthodontic appliances.