Skeletal maturation in different anteroposterior and vertical skeletal growth patterns in female subjects.

PURPOSE: The aim of the present work was to study the sequence of skeletal maturation in the various anteroposterior and vertical skeletal growth patterns and to detect whether differences existed between them. METHODS: Cephalograms of 861 growing and adolescent female patients were traced to categorize the subjects into 9 skeletal patterns. Each subject was assigned a skeletal maturational stage. Analysis of variance (ANOVA) followed by Bonferroni test were used to detect differences in the onset of the three growth stages (prepubertal, pubertal and postpubertal) between the 9 groups. The same statistical methods were used to detect differences between the mean ages at the three growth stages within each group. RESULTS: No statistically significant differences were found between the mean ages of pubertal and postpubertal growth stages between the 9 skeletal patterns. However, class III growers had a significantly earlier onset of prepubertal growth (10.25 ± 1.56 years) when compared to that of class II high angle cases (11.11 ± 1.67 years; P < 0.01). Also, significant differences were found between the mean ages at the three growth stages within the groups. CONCLUSION: A map was created defining the sequence of skeletal maturation for each skeletal growth pattern. This map defines clinically relevant differences in the starting time points and the optimum intervals of growth modification for each skeletal growth pattern.

The efficiency of mandibular mini-implants in reducing adverse effects of class II elastics in adolescent female patients: a single blinded, randomized controlled trial.

BACKGROUND: Excessive proclination of lower incisors and other undesirable consequences usually result from the use of class II elastics during orthodontic treatment. The purpose of this study was to attempt to limit the adverse effects of class II elastics by the use of mini implants placed in the mandibular arch in adolescent class II female patients. METHODS: The sample comprised 28 patients, (a mean age of 15.66 ± 2 years for intervention group and 15.1 ± 2.2 years for conventional group) with one-fourth or one-half unit class II canine relationship. The sample was divided into two equal groups. Randomization was carried out by a computer sequence generator with a 1:1 allocation ratio. In the intervention group, the mini implants were inserted between the lower second premolar and first molar, while the conventional group underwent regular class II elastics therapy. The active elastics treatment time was 8 months for both groups. Results were assessed by measurements from pre- and post-elastics lateral cephalometric radiographs. RESULTS: The change in L1 inclination (0.97 ± 0.92°) and L1 AP position (0.31 ± 0.63 mm) did not show a statistically significant difference between the two groups, but a statistically significant difference was found in the U1 retroclination (5.23 ± 1.92°) and U1 distal movement (4.05 ± 1.4 mm) [P ˂ 0.001] and [P ˂ 0.05] respectively in favor of the intervention group. CONCLUSION: Mini-implants in conjunction with class II elastics had no skeletal effect, mainly dentoalveolar and it did not prevent the proclination of lower incisors. There was more distal movement in the upper incisors in the skeletal anchorage group which helped in enhancing the camouflaging of class II malocclusion. TRIAL REGISTRATION: Trial registered "FUE.REC (10)/10-2018" at the FUE registration council for clinical trials/IOP Orthodontic Program October 2018.

Evaluation of the efficiency of computerized algorithms to formulate a decision support system for deepbite treatment planning.

INTRODUCTION: This study aimed to evaluate the efficiency of a newly constructed computer-based decision support system (DSS) on the basis of artificial intelligence technology and designed to plan treatment for patients with a deep overbite. METHODS: With the help of information technology, a DSS was developed specifically for treatment planning of deepbite malocclusion. The program inputs were the components and the contributing factors used commonly by the orthodontic clinicians in deepbite diagnosis. The program outputs were the treatment planning options for deepbite treatment. A total of 357 decisions made by the algorithm were evaluated for accuracy by comparing them to the actual treatment changes of 51 patients with a well-treated deepbite. RESULTS: The decisions made by the algorithm were precise, with 94.4% having a very good agreement with actual treatment changes determined using Cohen's kappa coefficient. CONCLUSIONS: The constructed DSS was shown to be an efficient tool for planning treatment of deep overbite malocclusion in the permanent dentition; thus, the artificial intelligence could be used to formulate a customized plan for orthodontic clinicians.

Deep overbite malocclusion: analysis of the underlying components.

INTRODUCTION: A deepbite malocclusion should not be approached as a disease entity; instead, it should be viewed as a clinical manifestation of underlying discrepancies. The aim of this study was to investigate the various skeletal and dental components of deep bite malocclusion, the significance of the contribution of each, and whether there are certain correlations between them. METHODS: Dental and skeletal measurements were made on lateral cephalometric radiographs and study models of 124 patients with deepbite. These measurements were statistically analyzed. RESULTS: An exaggerated curve of Spee was the greatest shared dental component (78%), significantly higher than any other component (P = 0.0335). A decreased gonial angle was the greatest shared skeletal component (37.1%), highly significant compared with the other components (P = 0.0019). A strong positive correlation was found between the ramus/Frankfort horizontal angle and the gonial angle; weaker correlations were found between various components. CONCLUSIONS: An exaggerated curve of Spee and a decreased gonial angle were the greatest contributing components. This analysis of deepbite components could help clinicians design individualized mechanotherapies based on the underlying cause, rather than being biased toward predetermined mechanics when treating patients with a deepbite malocclusion.