How much should incisors be decompensated? periodontal bone defects during presurgical orthodontic treatment in class III double-jaw orthognathic surgery patients

The Efficiency of Segmental Le Fort I Surgery in Clear Aligner Therapy of Skeletal Class III Deformity: A Pilot Study

INTRODUCTION

The purpose of this study was to investigate the efficiency of segmental Le Fort I osteotomy in clear aligner therapy of skeletal Class III deformities and to explore whether Le Fort I segmental osteotomy was effective for maxillary incisor axis correction and reduced the duration of perioperative orthodontics.

MATERIALS AND METHODS

Patients who had skeletal Class III deformities (ANB<0) treated with extraction of the maxillary first premolars, segmental Le Fort I osteotomy, and clear aligners therapy were included in this retrospective study. We measured the amount of tooth extraction space that was closed by surgery and recorded the preoperative orthodontic and total treatment duration. Lateral cephalograms were analyzed to measure changes of maxillary incisor inclination before treatment (T0), 1 week before surgery (T1), 1 week after surgery (T2), and after total orthodontic treatment (T3). Statistical analyses were performed, and the P value was set at 0.05.

RESULTS

The sample was composed of 15 patients aged 19 to 30 (M=22.9) years. The average preoperative orthodontic treatment duration was 16.2±5.22 mo, with 33.5 pairs of clear aligners. The gap at the extraction site decreased from 5.42±1.57 mm to 0.80±0.62 mm on average after surgery. U1-SN and U1-NA(deg) increased sparingly with preoperative decompensation, decreased in quantity after surgery, and then slightly increased with postoperative compensation (T20.05).

CONCLUSIONS

Le Fort I segmental osteotomy assisted decompensation of the upper anterior teeth and reduced the duration of preoperative orthodontics with clear aligners.

Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study

BACKGROUND

Gingival phenotype plays an important role in dental diagnosis and treatment planning. Traditionally, determining the gingival phenotype is done by manual probing of the gingival soft tissues, an invasive and time-consuming procedure. This study aims to evaluate the feasibility and accuracy of an alternatively novel, non-invasive technology based on the precise 3-dimension (3D) soft tissue reconstruction from intraoral scanning and cone beam computed tomography (CBCT) to predict the gingival biotype.

METHODS

As a proof-of-concept, Yorkshire pig mandibles were scanned, and the CBCT data were fed into a deep-learning model to reconstruct the teeth and surrounding bone structure in 3D. By overlaying the CBCT scan with the intraoral scans, an accurate superposition was created and used for virtual measurements of the soft tissue thickness. Meanwhile, gingival thicknesses were also measured by a periodontal probe and digital caliper on the buccal and lingual sides at 3 mm apical to the gingival margin of the posterior teeth and compared with the virtual assessment at the same location. The data obtained from virtual and clinical measurements were compared by Wilcoxon matched-pairs signed-rank analysis, while their correlation was determined by Pearson's r value. The Mann-Whitney U test was used for intergroup comparisons of the amount of difference.

RESULTS

Among 108 investigated locations, the clinical and virtual measurements are strongly positively correlated (r = 0.9656, P < 0.0001), and only clinically insignificant differences (0.066 ± 0.223 mm) were observed between the two assessments. There is no difference in the agreement between the virtual and clinical measurements on sexually matured samples (0.087 ± 0.240 mm) and pre-pubertal samples (0.033 ± 0.195 mm). Noticeably, there is a greater agreement between the virtual and clinical measurements at the buccal sites (0.019 ± 0.233 mm) than at the lingual sites (0.116 ± 0.215 mm).

CONCLUSION

In summary, the artificial intelligence-based virtual measurement proposed in this work provides an innovative technique potentially for accurately measuring soft tissue thickness using clinical routine 3D imaging systems, which will aid clinicians in generating a more comprehensive diagnosis with less invasive procedures and, in turn, optimize the treatment plans with more predictable outcomes.