MATERIALS AND METHODS: This was a prospective interventional study. Cone-beam computerized tomography (CBCT) images of 22 patients were taken before and after treatment by using Planmeca Promax 3D CBCT machine version 2.9.2 (Planmeca OY Helsinki, Finland). The condylar width, height, length, roof of glenoid fossa thickness, and all joint spaces were measured. The condylar position was determined based on Pullinger and Hollander formula. The condylar shape was determined as per Kinzinger et al. The condylar volume was calculated by using Mimics software (Materialize, Belgium).
STATISTICAL ANALYSIS: Data analysis was performed by using SPSS software version 24. Wilcoxon paired signed-rank test was used to compare the difference in temporomandibular joint morphology and condylar volume between pre- and post-treatment measurements. Chi-square test was used to compare the condylar position and shape.
RESULTS: The superior (p = 0.000 on the right side, p = 0.005 on the left side) and posterior joint spaces (p = 0.000 on both sides) were decreased after the treatment, respectively. The condyles were rotated upward and backward, thereby increasing the anterior joint spaces (p = 0.000 on both sides) after the treatment. The condylar volume increases after treatment, but no significant differences were observed (p = 0.903 on the right side, p = 0.062 on the left side).
CONCLUSION: The significant changes were observed in joint spaces. The condyles were more anteriorly placed before treatment. Condylar position and shape alter in response to ASSD treatment. The condylar volume did not show any significant change.
METHODS: This article tends to identify the global social indicators, compiled into a global index, and develop a correlation between the global social index created by using the human development index, social and political globalization, the global happiness index, and the quality of infrastructure, institutions, and individuals using the internet factors and its effect on global obesity.
RESULTS AND DISCUSSION: Our results identified a positive correlation between medium human development levels with obesity compared to low and very high human development levels. Economic stability due to rapid industrialization has increased the buying capacity and changed the global food system, which seems to be the major driver of the rise of global obesity.
CONCLUSION: The results decipher that global social indicators and overall social index have positively affected global obesity, which will help policymakers and governmental organizations monitor the obesity patterns across their regions by a significant contribution from globally influenced social factors.
METHODS: A retrospective case control study was conducted. A total of 100 subjects were included. Data was gathered from the databases of Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab II, respectively. Cone beam computed tomography scans of both cleft and non-cleft individuals were utilized to determine the MPS maturation stages. Romexis software version 3.8.2 was used to analyze the images.
RESULTS: The results of the binary logistic regression model were utilized to establish the relationship between the probability (P) of a specific event of interest (P(Y = 1)) and a linear combination of independent variables (Xs) using the logit link function. Potential factors such as age, gender, cleft, category of malocclusion, and MPS were chosen which could play a role in predicting the technique of RME in children with UCLP and non-UCLP. A subset of these variables was validated via multilayer feed forward neural network (MLFFNN).
CONCLUSIONS: The effectiveness of the hybrid biometric model created in this work, which combines bootstrap and BLR with R-syntax was evaluated in terms of how accurately it predicted a binary response variable. A validation method based on an MLFFNN was used to evaluate the precision of the generated model. This leads to a good outcome.
METHODS: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.
RESULTS: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.
CONCLUSION: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.