Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.

OBJECTIVE: This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodontic treatment and to compare the fabrication process of integrated tooth models (ITMs) with manual method. MATERIAL AND METHODS: Intraoral scans and corresponding CBCT scans before and after treatment were obtained from 15 patients who completed orthodontic treatment with premolar extraction. A total of 600 ITMs were generated using deep learning technology and manual methods by merging the intraoral scans and CBCT scans at pretreatment. Posttreatment intraoral scans were integrated into the tooth model, and the resulting estimated root positions were compared with the actual root position at posttreatment CBCT. Discrepancies between the estimated and actual root position including average surface differences, arch widths, inter-root distances, and root axis angles were obtained in both the deep learning and manual method, and these measurements were compared between the two methods. RESULTS: The average surface differences of estimated and actual ITMs in the manual method were 0.02 mm and 0.03 mm for the maxillary and mandibular arches, respectively. In the deep learning method, the discrepancies were 0.07 mm and 0.08 mm for the maxillary and mandibular arches, respectively. For the measurements of arch widths, inter-root distances, and root axis angles, there were no significant differences between estimated and actual models both in the manual and in the deep learning methods, except for some measurements. Comparing the two methods, only three measurements showed significant differences. The procedure times taken to obtain the measurements were longer in the manual method than in the deep learning method. CONCLUSION: Both deep learning and manual methods showed similar accuracy in the integration of intraoral scans and CBCT images. Considering time and efficiency, the deep learning automatic method for ITMs is highly recommended for clinical practice.

Geometric analysis of alveolar bone around the incisors after anterior retraction following premolar extraction.

OBJECTIVE: To evaluate changes in shape and alterations in thickness and vertical marginal bone levels of the alveolar bone around the maxillary and mandibular incisors before and after orthodontic treatment with premolar extraction using geometric morphometric analysis. MATERIALS AND METHODS: Thirty-six patients with Class I bialveolar protrusion who underwent orthodontic treatment with premolar extraction were included. Cone-beam computed tomographic scans were obtained from the patients before and after treatment. Five fixed landmarks and 70 semilandmarks were used to represent the morphology of the alveolar bone around the maxillary and mandibular incisors. The coordinates of the landmarks of the alveolar bones were generated by Procrustes fit. The labial and lingual alveolar bone thicknesses around the maxillary and mandibular incisors and vertical marginal bone level were assessed quantitatively. RESULTS: There was a significant difference in shape change of the alveolar bone before and after treatment. The deformation grid of the thin plate spline showed that the thickness and vertical marginal bone decreased on the lingual side after treatment. Shape changes were greater for the lingual alveolar bone on the mandibular incisor than for the maxillary incisors. CONCLUSIONS: Orthodontic treatment with premolar extraction might cause loss of alveolar bone around the maxillary and mandibular incisors. Careful consideration is needed to avoid iatrogenic degeneration of periodontal support around the incisors, particularly in the lingual area.

Change of lip-line cant after 1-jaw orthognathic surgery in patients with mandibular asymmetry.

INTRODUCTION: The purpose of this study was to investigate the change of lip-line cant (LLC) after 1-jaw orthognathic surgery in mandibular asymmetry patients. METHODS: Preoperative and postoperative data of 22 patients having 1-jaw orthognathic surgery, with menton deviation over 2 degrees before the surgery, were our subjects. LLC was measured in the preoperative and postoperative frontal photographs, and its change was correlated with various craniofacial measurements obtained from preoperative and postoperative frontal cephalograms and maxillofacial 3-dimensional computed tomography images. RESULTS: Although these subjects had 2.4 degrees of LLC on average before surgery, LLC improved to 0.5 degrees after surgery, and the change (1.9 degrees ) was statistically significant. In the correlation analysis, preoperative LLC showed positive correlations with menton deviation and mandibular anterior occlusal plane cant. In the correlation analysis of LLC change, it had positive correlations with preoperative LLC and mandibular anterior occlusal plane cant and preoperative and postoperative change of menton deviation. CONCLUSIONS: These results suggest that LLC is present with chin deviation, even without significant maxillary canting, and can be improved considerably by 1-jaw surgery alone.