ABSTRACT

Objectives

To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model.

Materials and Methods

Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared.

Results

On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability.

Conclusions

AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable.

ABSTRACT

Objectives

To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from this method.

Materials and Methods

Based on a GCN, learning the features from 100 three-dimensional digital occlusal models under supervision of the palatal stable structure labels that were manually annotated by senior specialists, the palatal stable structure was automatically segmented. The average Hausdorff distance was calculated to assess the difference between automatic and manual segmentations. Tooth position and angulation, including rotation, tip, and torque, of bilateral upper first molars and central incisors were obtained to measure the clinical error of automatic superimposition. Reliability was calculated by intraclass correlation coefficient (ICC).

Results

The average Hausdorff distance was 0.36 mm between automatic and manual segmentations of the palatal stable region and was larger than the intraexaminer and interexaminer deviations. The tooth position deviation was <0.32 mm, and the tooth angulation difference was <0.26° for tip and torque, and 0.46–0.61° in rotation. ICCs, used for assessment of reliability, ranged from 0.82 to 0.99 in all variables.

Conclusions

The GCN-based MDM superimposition is an efficient method for the assessment of tooth movement in adults. The clinical error in tooth position and angulation induced by the method was clinically acceptable. Reliability was as high as manual segmentation.

ABSTRACT

Objectives

To determine factors associated with treatment duration in impacted maxillary canines (IMCs) using orthopantomography and cone beam computed tomography.

Materials and Methods

Seventeen linear and angular measurements were evaluated using pretreatment radiographic images of 64 patients (83 IMCs). Treatment duration from surgical exposure until the canine appeared in the oral cavity and until achieving ideal alignment were recorded in months.

Results

Average treatment duration until appearance in the mouth was 14.61 ± 9.28 months and 26.98 ± 12.44 months until ideal alignment. Buccally impacted canines emerged in 6.56 ± 3.24 months and were ideally aligned in 19.44 ± 8.49 months in comparison with palatally positioned at 16.34 ± 9.19 and 28.91 ± 12.77 months, respectively. Canines localized around lateral incisors had shorter treatment times than those localized around central incisors. Canines that were inclined >30° to the sagittal medial line (SML) had significantly longer treatment time until initial appearance in the mouth (16.31 ± 9.32) compared with those inclined less (10.82 ± 8.92). Linear regression analyses demonstrated that these factors were predictors for both treatment duration until emergence and ideal alignment of the canine.

Conclusions

Treatment duration for IMCs is lengthy, particularly for palatally and mesially positioned canines and those with greater inclinations. Palatally located canines typically require twice as much time to emerge as those positioned buccally. Canines located nearer the central incisor area take twice as much time to emerge as those located in the lateral incisor area.

This listing of academic opportunities is provided as a free service to the profession and the community.

MINNESOTA

Mayo Clinic Dental Specialties

Mayo Clinic Building, Fourth Floor

200 First Street SW

Rochester, MN 55905

Apply online: www.jobs.mayoclinic.org

Position #304265

Include: Cover letter, CV, 3 references

Rank: Full-time Consultant

Requirements: Candidates must be board-certified/board-eligible in orthodontics with an interest in joining a busy quality-oriented orthodontic practice. The division is seeking an individual with a strong interest in a clinical position and clinical instruction, with opportunities to facilitate clinical and basic science research and work collaboratively with a variety of specialists.

Lip incompetence resolved by active vertical control in nonsurgical treatment of a protrusion case with vertical maxillary excess

Johnny JL Liaw,

Jae Hyun Park,

Fang Fang Tsai,

Betty MY Tsai, and

Wendy WT Liao