Analysis of different characteristics of smile

Introduction: Analysis of smile is imperative in the diagnosis and treatment planning phases of aesthetic dentistry.Aim: To evaluate the components of smile among students of a dental institution.Methods: Frontal view digital photographs with posed smile of 157 dental students were assessed using Adobe Photoshop7.0. Smile characteristics evaluated included; smile line, smile arc, smile design, upper lip curvature, labiodental relationship and number of teeth displayed. Data were analyzed using SPSS version 23.0. Pearson chi-square test was used to determine the gender based differences for various parameters.Results: Average smile line (43.3%), consonant smile arcs (45.2%), cuspid smiles (45.9%), upward lip curvature (43.9%), maxillary anterior teeth not covered by lower lip (60.5%) and teeth displayed up to first premolars (35.7%). Gender based differences were not statistically significant except for smile arc (p value = 0.02) and number of teeth displayed (p value \u3c 0.001). There was a significant relationship between lip curvature and smile pattern (p value \u3c 0.001) and lip curvature and smile arc (p value = 0.01) revealing that upward lip curvature was associated with commissure type smiles and consonant smile arcs.Conclusions: The smile characteristics should be considered before beginning the aesthetic treatment of the patient to obtain adequate results in oral rehabilitation

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0–1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide

Asymmetric extractions in orthodontics

INTRODUCTION: Extraction decisions are extremely important in during treatment planning. In addition to the extraction decision orthodontists have to choose what tooth should be extracted for the best solution of the problem and the esthetic/functional benefit of the patient. OBJECTIVE: This article aims at reviewing the literature relating the advantages, disadvantages and clinical implications of asymmetric extractions to orthodontics. METHODS: Keywords were selected in English and Portuguese and the EndNote 9 program was used for data base search in PubMed, Web of Science (WSc) and LILACS. The selected articles were case reports, original articles and prospective or retrospective case-control studies concerning asymmetrical extractions of permanent teeth for the treatment of malocclusions. CONCLUSION: According to the literature reviewed asymmetric extractions can make some specific treatment mechanics easier. Cases finished with first permanent molars in Class II or III relationship in one or both sides seem not to cause esthetic or functional problems. However, diagnosis knowledge and mechanics control are essential for treatment success