Early Prediction of the Need for Orthognathic Surgery in Patients With Repaired Unilateral Cleft Lip and Palate Using Machine Learning and Longitudinal Lateral Cephalometric Analysis Data.

ABSTRACT: The purpose of this study was to determine the cephalometric predictors of the future need for orthognathic surgery in patients with repaired unilateral cleft lip and palate (UCLP) using machine learning. This study included 56 Korean patients with UCLP, who were treated by a single surgeon and a single orthodontist with the same treatment protocol. Lateral cephalograms were obtained before the commencement of orthodontic/orthopedic treatment (T0; mean age, 6.3 years) and at at least of 15 years of age (T1; mean age, 16.7 years). 38 cephalometric variables were measured. At T1 stage, 3 cephalometric criteria (ANB ≤ -3°; Wits appraisal ≤ -5 mm; Harvold unit difference ≥34 mm for surgery group) were used to classify the subjects into the surgery group (n = 10, 17.9%) and non-surgery group (n = 46, 82.1%). Independent t-test was used for statistical analyses. The Boruta method and XGBoost algorithm were used to determine the cephalometric variables for the prediction model. At T0 stage, 2 variables exhibited a significant intergroup difference (ANB and facial convexity angle [FCA], all P < 0.05). However, 18 cephalometric variables at the T1 stage and 14 variables in the amount of change (ΔT1-T0) exhibited significant intergroup differences (all, more significant than P < 0.05). At T0 stage, the ANB, PP-FH, combination factor, and FCA were selected as predictive parameters with a cross-validation accuracy of 87.4%. It was possible to predict the future need for surgery to correct sagittal skeletal discrepancy in UCLP patients at the age of 6 years.

Prediction of the Need for Orthognathic Surgery in Patients With Cleft Lip and/or Palate.

The purpose of this study was to determine the cephalometric variables that can predict the future need for orthognathic surgery or distraction osteogenesis in Korean male patients with nonsyndromic cleft lip and alveolus (CLA) and unilateral (UCLP) and bilateral cleft lip and palate (BCLP). A total of 131 patients who were treated by one surgeon and one orthodontist using identical protocol were divided into CLA group (n = 35), UCLP group (n = 56), and BCLP group (n = 40). Lateral cephalograms were taken before secondary alveolar bone graft (T0; mean age, 9.3 years) and at the minimum of 15 years of age (T1; mean age, 17.3 years). The cephalometric variables of these cephalograms were measured. At T1 stage, 3 cephalometric criteria were used to divide the subjects into surgery and nonsurgery groups (ANB ≤ -3 degrees; Wits appraisal ≤ -5 mm; Harvold unit difference ≥ 34 mm for surgery group). The feature wrapping method was used to determine the cephalometric variables at T0 stage for a prediction model. At T1 stage, 27 (20.6%) of 131 subjects required surgical intervention to correct their sagittal skeletal discrepancies. Frequency was significantly different among the CLA, UCLP, and BCLP groups (8.5%, 21.4%, and 30.0%, respectively; P < 0.05; [CLA, UCLP] < [UCLP, BCLP]). A total of 10 cephalometric variables of T0 stage were selected as predictors, and weighted classification accuracy was 77.3%. The frequency of surgical intervention increased with cleft severity. Ten cephalometric variables might be regarded as effective predictors of the future need for surgery to correct their sagittal skeletal discrepancies.

Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data.

The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions.

OBJECTIVE: To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. METHODS: Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. RESULTS: Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. CONCLUSIONS: Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

Current advances of epigenetics in periodontology from ENCODE project: a review and future perspectives.

BACKGROUND: The Encyclopedia of DNA Elements (ENCODE) project has advanced our knowledge of the functional elements in the genome and epigenome. The aim of this article was to provide the comprehension about current research trends from ENCODE project and establish the link between epigenetics and periodontal diseases based on epigenome studies and seek the future direction. MAIN BODY: Global epigenome research projects have emphasized the importance of epigenetic research for understanding human health and disease, and current international consortia show an improved interest in the importance of oral health with systemic health. The epigenetic studies in dental field have been mainly conducted in periodontology and have focused on DNA methylation analysis. Advances in sequencing technology have broadened the target for epigenetic studies from specific genes to genome-wide analyses. CONCLUSIONS: In line with global research trends, further extended and advanced epigenetic studies would provide crucial information for the realization of comprehensive dental medicine and expand the scope of ongoing large-scale research projects.

Transcriptome and methylome analysis of periodontitis and peri-implantitis with tobacco use.

Peri-implantitis is similar to periodontitis in both symptoms and treatment; however, their level of similarity remains controversial. Here, we compared multiple cases of periodontitis and peri-implantitis through transcriptome and methylome profiling, and analyzed the effects of smoking as a typical risk factor. Human gingival tissues were obtained from 20 patients with periodontitis or peri-implantitis via periodontal surgical procedures. Total RNA and genomic DNA were isolated, and transcriptome and methylome datasets were generated. Comprehensive analysis of differential gene expression, DNA methylation, and protein-protein interactions indicated that periodontitis and peri-implantitis share biological similarities; however, hierarchical clustering between the two disease groups revealed distinct molecular characteristics. These differences might be related to structural differences in natural tooth-bone and implant-bone. Additionally, smoking differentially affected periodontitis and peri-implantitis in terms of host-defense mechanism impairment. Within the limitations of this study, the results provide evidence that peri-implantitis is distinct from periodontitis and that smoking potentially affects disease progression. Our study provides a foundation for the rational design of a large-scale study in the future for a more comprehensive analysis that includes microbiome and clinical data.

Prognosis prediction for Class III malocclusion treatment by feature wrapping method.

OBJECTIVE: To use the feature wrapping (FW) method to identify which cephalometric markers show the highest classification accuracy in prognosis prediction for Class III malocclusion and to compare the prediction accuracy between the FW method and conventional statistical methods such as discriminant analysis (DA). MATERIALS AND METHODS: The sample set consisted of 38 patients (15 boys and 23 girls, mean age 8.53 +/- 1.36 years) who were diagnosed with Class III malocclusion and received both first-phase (orthopedic) and second-phase (fixed orthodontic) treatments. Lateral cephalograms were taken before (T0) and after first-phase treatment (T1) and after second-phase treatment and retention (T2). Based on the measurements taken at the T2 stage, the patients were allocated into good (n = 20) or poor (n = 18) prognosis groups. Forty-six cephalometric variables on T0 lateral cephalograms were analyzed by the FW method to identify key determinants for discriminating between the two groups. Sequential forward search (SFS) algorism and support vector machine (SVM) were used in conjunction with the FW method to improve classification accuracy. To compare the prediction accuracy of the FW method with conventional statistical methods, DA was performed for the same data set. RESULTS: AB to mandibular plane angle ( degrees ) and A to N-perpendicular (mm) were selected as the most accurate cephalometric predictors by both the FW and DA methods. However, classification accuracy was higher with the FW method (97.2%) compared with DA (92.1%), because the FW method with SFS and SVM has a more precise classification algorithm. CONCLUSIONS: The FW method, which uses a learning algorithm, might be an effective alternative to DA for prognosis prediction.

The disturbance in dynamic property in the reconstructed state space during nitrous oxide administration.

Integration of neural information could be considered a crucial element in maintaining consciousness. Although consciousness is usually maintained during sedation, strictly speaking, the sedative state is different from consciousness. Nitrous oxide (N2O) has been used widely to sedate patients for dental procedures. However, its mechanism remains unclear. The aim of this study was to analyze the extent of disturbance in the integration of brain activities during N2O administration on the basis of projection to the reconstructed state space. Electroencephalography (EEG) data were obtained from 15 patients undergoing N2O (50%) administration. The correlation dimension in the reconstructed state space was quantified and correlation dimensions were compared according to consciousness levels (awake state vs. sedative state induced by N2O). The correlation dimension of alpha and gamma band EEG in the sedative state showed statistical significance at around 15 and 5 of the embedding dimension during N2O administration. The difference was the most prominent in alpha band EEG. This result indicates that the sedative state shows a higher dimensional trajectory in the reconstructed state space that is brain band specific. These data also indicate that the dimensional dynamic property has a different pattern during sedation induced by N2O compared with that at the awake state. The disturbance in information integration may explain the mild cognitive disturbance of N2O.

Transcriptomics and methylomics in chronic periodontitis with tobacco use: a pilot study.

BACKGROUND: Accumulating evidence suggests that tobacco smoking affects the susceptibility to and severity of chronic periodontitis. Epigenetics may explain the role of smoking in the development and progress of periodontal disease. In this study, we performed transcriptomic and methylomic analyses of non-periodontitis and periodontitis-affected gingival tissues according to smoking status. METHODS: Human gingival tissues were obtained from 20 patients, including non-smokers with and without periodontitis (n = 5 per group) and smokers with and without periodontitis (n = 5 per group). Total RNA and genomic DNA were isolated, and their quality was validated according to strict standards. The Illumina NextSeq500 sequencing system was used to generate transcriptome and methylome datasets. RESULTS: Comprehensive analysis, including between-group correlation, differential gene expression, DNA methylation, gene set enrichment, and protein-protein interaction, indicated that smoking may change the transcription and methylation states of extracellular matrix (ECM) organization-related genes, which exacerbated the periodontal condition. CONCLUSIONS: Our results suggest that smoking-related changes in DNA methylation patterns and subsequent alterations in the expression of genes coding for ECM components may be causally related to the increased susceptibility to periodontitis in smokers as they could influence ECM organization, which in turn may have an effect on disease characteristics.

The Improvement and Completion of Outcome index: A new assessment system for quality of orthodontic treatment.

OBJECTIVE: Given the considerable disagreement between the Peer Assessment Rating (PAR) index and the American Board of Orthodontics Cast-Radiograph Evaluation, we aimed to develop a novel assessment system-the Improvement and Completion of Outcome (ICO) index-to evaluate the outcome of orthodontic treatment. METHODS: Sixteen criteria from 4 major categories were established to represent the pretreatment malocclusion status, as well as the degree of improvement and level of completion of outcome during/after treatment: dental relationship (arch length discrepancy, irregularity, U1-SN, and IMPA); anteroposterior relationship (overjet, right and left molar position, ANB); vertical relationship (anterior overbite, anterior open-bite, lateral open-bite, SN-MP); and transverse relationship (dental midline discrepancy, chin point deviation, posterior cross-bite, occlusal plane cant). The score for each criterion was defined from 0 or -1 (worst) to 5 (ideal value or normal occlusion) in gradations of 1. The sum of the scores in each category indicates the area and extent of the problems. Improvement and completion percentages were estimated based on the pre- and post-treatment total scores and the maximum total score. If the completion percentage exceeded 80%, treatment outcome was considered successful. RESULTS: Two cases, Class I malocclusion and skeletal Class III malocclusion, are presented to represent the assessment procedure using the ICO index. The difference in the level of improvement and completion of treatment outcome can be clearly explained by using 2 percentage values. CONCLUSIONS: Thus, the ICO index enables the evaluation of the quality of orthodontic treatment objectively and consecutively throughout the entire treatment process.