Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning.

The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.

Racial and gender differences of cervical vertebral maturation staging between African Americans versus Caucasian patients of various age groups: A retrospective study.

BACKGROUND AND OBJECTIVE: The primary purpose of this study was to compare CVM staging between African American (AA) and Caucasian (CC) subjects, grouped based on gender. The secondary objectives were to conduct a CVM comparison of (1) male vs. female subjects, grouped based on their race, and (2) AA vs. CC subjects, categorized based on their age group. MATERIALS AND METHODS: All patients between 8-18 years of age (University of XXX), meeting the criteria, were included between the period of the year 2007 to 2020. Three blinded independent evaluators analysed the lateral cephalograms for a 6-stage CVM system (CS1 to CS6) as described by McNamara and Franchi. Samples were divided based on race, gender, and age to conduct the statistical analysis for racial and gender comparisons. RESULTS: Out of the initial 1,300 lateral cephalograms, 1,276 with the mean age: 12.7 years SD 2.5 years, and median CVM: 4 (IQR: 25% percentile- 2 and 75% percentile- 4) were included. Gender-specific racial age comparison showed no significant differences for male subjects for most of the CVM stages (P>0.05). Overall and race-specific gender comparison of age showed significant differences for almost all CVM stages (P<0.05). No significant difference of CVM was found on racial comparison for overall, females (P=0.6131) or males (P=0.0825) subjects. On age-specific racial comparison, AA girls (8-10 years) were skeletally more mature than CC girls (P=0.0143); over 14-year-old CC girls (P<0.0001) and over 16-year-old CC boys (P=0.0008), being skeletally more mature than AA boys of the same age. CONCLUSIONS: There was no significant difference between AA and CC subjects on gender-specific comparisons for most of the CVM stages. A significant difference of age was observed for most of the CVM stage between male and female subjects on race-specific comparisons. Female patients were ahead in skeletal maturity as compared to males for each CVM stage. The CVM for 8-12-year-old AA females was significantly higher than that of the CC females, whereas the CVM for>14-year-old CC females was significantly higher than AA females.

Parents' Past Bonding Experience with Their Parents Interacts with Current Parenting Stress to Influence the Quality of Interaction with Their Child.

Healthy dyadic interactions serve as a foundation for child development and are typically characterised by mutual emotional availability of both the parent and child. However, several parental factors might undermine optimal parent-child interactions, including the parent's current parenting stress levels and the parent's past bonding experiences with his/her own parents. To date, no study has investigated the possible interaction of parenting stress and parental bonding history with their own parents on the quality of emotional availability during play interactions. In this study, 29 father-child dyads (18 boys, 11 girls; father's age = 38.07 years, child's age = 42.21 months) and 36 mother-child dyads (21 boys, 15 girls; mother's age = 34.75 years, child's age = 41.72 months) from different families were recruited to participate in a 10-min play session after reporting on their current parenting stress and past care and overprotection experience with their parents. We measured the emotional availability of mother-child and father-child play across four adult subscales (i.e., sensitivity, structuring, non-intrusiveness, non-hostility) and two child subscales (i.e., involvement and responsiveness). Regression slope analyses showed that parenting stress stemming from having a difficult child predicts adult non-hostility, and is moderated by the parents' previously experienced maternal overprotection. When parenting stress is low, higher maternal overprotection experienced by the parent in the past would predict greater non-hostility during play. This finding suggests that parents' present stress levels and past bonding experiences with their parents interact to influence the quality of dyadic interaction with their child.