The aim of this cross-sectional outcome study using retrospective data capture of treatment histories was to examine the characteristics of young children with unilateral cleft lip and palate who had poor dental arch relationship (i.e., Goslon 5). The study sample comprised 120 children born with nonsyndromic complete unilateral cleft lip and palate between 1995 and 2003, and were aged between 5.0 and 7.0 years (mean age, 5.1 years) at the time of data collection. The dental arch relationship was assessed using the Goslon yardstick from intraoral dental photographs. An independent investigator recorded treatment histories from the clinical notes. The inter- and intraexaminer agreements evaluated by weighted kappa statistics were high. There was no association between dental arch relationship and the type of presurgical orthopedics or pharyngeal flap. Dental arch relationship was associated with the initial cleft size (odds ratio, OR = 1.3; 95% confidence interval, CI = 1.1-1.5, p < 0.01), surgeon grade for palate repair (OR = 5.0, 95% CI = 1.2-19.9, p < 0.05), and primary gingivoperiosteoplasty (OR = 2.8, 95% CI = 1.0-8.1, p = 0.05). These data suggest that intraoral dental photographs provide a reliable method for rating dental arch relationship. Wide initial cleft, high-volume surgeon, and primary gingivoperiosteoplasty are predictors of poor dental arch relationship outcome in young children with unilateral cleft lip and palate. These findings may improve treatment outcome by modifying the treatment protocol for patients with unilateral cleft lip and palate.

Diagnosis and treatment planning forms the crux of orthodontics, which orthodontists gain with years of expertise. Machine Learning (ML), having the ability to learn by pattern recognition, can gain this expertise in a very short duration, ensuring reduced error, inter–intra clinician variability and good accuracy. Thus, the aim of this study was to construct an ML predictive model to predict a broader outline of the orthodontic diagnosis and treatment plan. The sample consisted of 700 case records of orthodontically treated patients in the past ten years. The data were split into a training and a test set. There were 33 input variables and 11 output variables. Four ML predictive model layers with seven algorithms were created. The test set was used to check the efficacy of the ML-predicted treatment plan and compared with that of the decision made by the expert orthodontists. The model showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier algorithms showing the highest accuracy ranging from 87–93%. Yet in their infancy stages, Machine Learning models could become a valuable Clinical Decision Support System in orthodontic diagnosis and treatment planning in the future.

Introduction: Non Syndromic Cleft lip/Palate is a common congenital anomaly with significant medical, psychological, social and economic ramifications. It is an example of complex genetic trait. There is sufficient evidence to hypothesize that disease locus for this condition can be identified by candidate genes. The purpose of this study was to test whether TGFB3 rs2300607 (IVSI+ 5321) gene variant was involved in the etiology of Non Syndromal Cleft lip/Palate. Materials and methods: Blood samples were collected with informed consent from 25 subjects having Non Syndromic Cleft lip/Palate and 25 controls .Genomic DNA was extracted from the blood samples, Polymerase Chain Reaction was performed (PCR) and digestion products were evaluated.Results: The Results showed a positive correlation between TGFB3 rs2300607 (IVSI+ 5321) variant and Non Syndromal Cleft lip/Palate patients.Conclusion: TGFB3 rs2300607 (IVSI+ 5321) gene variant may be a good screening marker for Non Syndromal Cleft lip /Palate.

OBJECTIVE: The main objective of this study is to get a wider and clearer idea about the relationship between sella turcica bridging and the type of dental anomalies related to size, shape, number, structure and eruption of teeth. MATERIALS AND METHODS: For the present study, 50 pretreatment lateral cephalometric radiographs showing complete sella turcica bridging were retrieved from the 500 existing case records of patients. The control group consisted of 50 pretreatment lateral cephalograms without sella turcica bridging retrieved from the same case records by using simple random sampling. After collection of the samples, retrospective study was performed with the analysis of patient records to assess any associated dental anomaly in patients with sella turcica bridging and patients without sella turcica bridging. Shafer's classification of morphological variations in size, shape, structure, number and eruption of teeth was used to analyze and group the dental anomalies. RESULTS: The incidence of dental anomalies related to number and size of teeth was found to be higher in cases with sella turcica bridging. CONCLUSION: Lateral cephalogram is used by orthodontist routinely for diagnosis and treatment planning; it can be used as a prediction tool for dental anomalies as well. Early detection of skeletal anomalies can be used to forecast the presence of dental anomalies later in life, which will help the clinician to adopt preventive measures.

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