Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

Objectives: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models.

Materials And Methods: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded.

Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.

Objectives: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI).

Materials And Methods: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually.

Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method.

Objectives: To develop a facial growth prediction model incorporating individual skeletal and soft tissue characteristics.

Materials And Methods: Serial longitudinal lateral cephalograms were collected from 303 children (166 girls and 137 boys), who had never undergone orthodontic treatment. A growth prediction model was devised by applying the multivariate partial least squares (PLS) algorithm, with 161 predictor variables.

Time series analysis.

Introduction: This article describes a simple method of applying a time series analysis to sample data sets using a free and open statistical software program, Language R.

Methods: Records of new patients who visited 2 different university-affiliated orthodontic departments in 2 different countries were collected. Time series analysis was performed by applying Language R software.

Assessment of reliability in orthodontic literature.

Objectives: To map the statistical methods applied to assess reliability in orthodontic publications and to identify possible trends over time.

Materials And Methods: Original research articles published in 2009 and 2019 in a subset of orthodontic journals were downloaded. Publication characteristics, including publication year, number of authors, single vs multicenter study, geographic origin of the study, statistician involvement, study category, subject category, types of reliability assessment, and statistical methods applied to assess reliability, were recorded.

Evaluation of an automated superimposition method based on multiple landmarks for growing patients.

Objectives: To determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes.

Materials And Methods: Serial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment.

Evaluation of automated cephalometric analysis based on the latest deep learning method.

Objectives: To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI).

Materials And Methods: This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied.

Automated identification of cephalometric landmarks:

Objectives: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners.

Materials And Methods: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified.

Automated identification of cephalometric landmarks: .

Objective: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks.

Materials And Methods: A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks.

Predicting soft tissue changes after orthognathic surgery: .

Objectives: To develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables.

Materials And Methods: The subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods-the partial least squares (PLS) and the sparse partial least squares (SPLS) methods-were used to construct prediction equations.

A sparse principal component analysis of Class III malocclusions.

Objectives: To identify the most characteristic variables out of a large number of anatomic landmark variables on three-dimensional computed tomography (CT) images. A modified principal component analysis (PCA) was used to identify which anatomic structures would demonstrate the major variabilities that would most characterize the patient.

Materials And Methods: Data were collected from 217 patients with severe skeletal Class III malocclusions who had undergone orthognathic surgery.

How to test validity in orthodontic research: a mixed dentition analysis example.

Introduction: The data used to test the validity of a prediction method should be different from the data used to generate the prediction model. In this study, we explored whether an independent data set is mandatory for testing the validity of a new prediction method and how validity can be tested without independent new data.

Methods: Several validation methods were compared in an example using the data from a mixed dentition analysis with a regression model.

A more accurate soft-tissue prediction model for Class III 2-jaw surgeries.

Introduction: The use of bimaxillary surgeries to treat Class III malocclusions makes the results of the surgeries more complicated to estimate accurately. Therefore, our objective was to develop an accurate soft-tissue prediction model that can be universally applied to Class III surgical-orthodontic patients regardless of the type of surgical correction: maxillary or mandibular surgery with or without genioplasty.

Methods: The subjects of this study consisted of 204 mandibular setback patients who had undergone the combined surgical-orthodontic correction of severe skeletal Class III malocclusions.

Testing a better method of predicting postsurgery soft tissue response in Class II patients: A prospective study and validity assessment.

Objective: (1) To perform a prospective study using a new set of data to test the validity of a new soft tissue prediction method developed for Class II surgery patients and (2) to propose a better validation method that can be applied to a validation study.

Materials And Methods: Subjects were composed of two subgroups: training subjects and validation subjects. Eighty Class II surgery patients provided the training data set that was used to build the prediction algorithm.

Survival analysis of a miniplate and tube device designed to provide skeletal anchorage.

Introduction: The aim of this prospective cohort study was to compute the clinical survival and complication rates of a miniplate with a tube device (C-tube) used for orthodontic treatment.

Methods: From August 2003 to May 2012, 217 patients were recruited. They received 341 C-tube miniplates.

A better statistical method of predicting postsurgery soft tissue response in Class II patients.

Objective: To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients.

Materials And Methods: The subjects comprise 80 patients who had undergone surgical correction of severe Class II malocclusions. Using 228 predictor and 64 soft tissue response variables, we applied two multivariate methods of forming prediction equations, the conventional ordinary least squares (OLS) method and the partial least squares (PLS) method.

How to report reliability in orthodontic research: Part 2.

Proper statistical analysis is an absolutely essential tool for both clinicians and researchers attempting to implement evidence-based decisions. When analyzing reliability, statistical graphic representation is the best method. Other previously published error studies of 2-dimensional measurements, such as cephalometric landmarks, have inappropriately applied 1-dimensional approaches, such as linear or angular measurements.

How to report reliability in orthodontic research: Part 1.

In reporting reliability, duplicate measurements are often needed to determine if measurements are sufficiently in agreement among the observers (interobserver agreement) and/or within the same observer (intraobserver agreement). Some reports are often analyzed inappropriately using paired t tests and/or correlation coefficients. The aim of this article is to highlight the statistical problems of reliability testing using paired t tests and correlation coefficients and to encourage good reliability reporting within orthodontic research.

Differentiation and characteristics of undifferentiated mesenchymal stem cells originating from adult premolar periodontal ligaments.

Objective: The purpose of this study was to investigate the isolation and characterization of multipotent human periodontal ligament (PDL) stem cells and to assess their ability to differentiate into bone, cartilage, and adipose tissue.

Methods: PDL stem cells were isolated from 7 extracted human premolar teeth. Human PDL cells were expanded in culture, stained using anti-CD29, -CD34, -CD44, and -STRO-1 antibodies, and sorted by fluorescent activated cell sorting (FACS).

Do Class III patients have a different growth spurt than the general population?

Introduction: Understanding the timing and length of the growth spurt of Class III prognathic patients is fundamental to the strategy of interceptive orthopedic orthodontics as well as to the timing of orthognathic surgery. Consequently, this study was undertaken to determine whether there are any significant differences in the stature growth pattern of Class III subjects compared with non-Class III subjects and the general population.

Methods: Twelve-year longitudinal stature growth data were collected for 402 randomly selected adolescents in the general population, 55 Class III mandibular prognathic patients, and 37 non-Class III patients.

A more accurate method of predicting soft tissue changes after mandibular setback surgery.

Purpose: To propose a more accurate method to predict the soft tissue changes after orthognathic surgery.

Patients And Methods: The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordinary least-squares (OLS) and the partial least-squares (PLS) methods.

The relationship between temporomandibular joint disk displacement and mandibular asymmetry in skeletal Class III patients.

Objective: To investigate the relationship between temporomandibular joint disk displacement (TMJ DD) and facial asymmetry in skeletal Class III patients.

Materials And Methods: The subjects comprised 97 skeletal Class III adult patients seeking orthodontic treatment. In addition to the routine lateral and posteroanterior (PA) cephalograms, and regardless of the TMJ status, each subject consented to magnetic resonance imaging (MRI) to evaluate their TMJs.

Identification of enoxacin as an inhibitor of osteoclast formation and bone resorption by structure-based virtual screening.

An interaction between the B2 subunit of vacuolar H(+)-ATPase (V-ATPase) and microfilaments is required for osteoclast bone resorption. An atomic homology model of the actin binding site on B2 was generated and molecular docking simulations were performed. Enoxacin, a fluoroquinolone antibiotic, was identified and in vitro testing demonstrated that enoxacin blocked binding between purified B2 and microfilaments.