ABSTRACT
The aim of this cross-sectional study was to evaluate the electromyographic activity of the superior orbicularis oris muscle both in children surgically treated for bilateral complete cleft lip and palate (BCCLP) as well as in subjects without BCCLP. The study comprised 77 children aged 6.6 to 12.5 years. All the patients with clefts had previously undergone lip and palate surgery. The upper lip electromyographic (EMG) assessments were made with a DAB-Bluetooth device (Zebris Medical GmbH, Germany) at rest, while swallowing saliva, protruding lips and compressing lips. EMG measurements were also made when the subjects produced phonemes /p/, /b/, and /m/ with the vowel /a/. The Mann-Whitney U test was applied to statistically analyze the EMG values. Significantly higher median upper lip EMG activity under working conditions such as swallowing saliva, lip compression, and production of the phoneme /p/ with the vowel /a/ was observed in patients with BCCLP compared to those without a cleft. The results of the study showed that the upper lip muscle activity increases in children with BCCLP when swallowing saliva, compressing lips and during some speech movement tasks. This may be important in the aspect of the effect of surgical lip repair on the craniofacial growth.
ABSTRACT
Innovative computer techniques are starting to be employed not only in academic research, but also in commercial production, finding use in many areas of dentistry. This is conducive to the digitalization of dentistry and its increasing treatment and diagnostic demands. In many areas of dentistry, such as orthodontics and maxillofacial surgery, but also periodontics or prosthetics, only a correct diagnosis ensures the correct treatment plan, which is the only way to restore the patient's health. The diagnosis and treatment plan is based on the specialist's knowledge, but is subject to a large, multi-factorial risk of error. Therefore, the introduction of multiparametric pattern recognition methods (statistics, machine learning and artificial intelligence (AI)) is a great hope for both the physicians and the patients. However, the general use of clinical decision support systems (CDSS) in a dental clinic is not yet realistic and requires work in many aspects - methodical, technological and business. The article presents a review of the latest attempts to apply AI, such as CDSS or genetic algorithms (GAs) in research and clinical dentistry, taking under consideration all of the main dental specialties. Work on the introduction of public CDSS has been continued for years. The article presents the latest achievements in this field, analyzing their real-life application and credibility.
Subject(s)
Dentistry/trends , Machine Learning , HumansABSTRACT
BACKGROUND: Adhesive materials used in orthodontics have contributed to the broadening of treatment options with fixed braces. The adhesive materials physically and chemically bond to the enamel surface and orthodontic bracket base, which, apart from offering advantages, also entails the risk of enamel damage when removing these materials from the tissue surface after the treatment is complete. OBJECTIVES: The objective of this study was to assess how the bond strength of adhesive materials affects enamel thickness after removing brackets and whether the type of bonding system affects the amount of adhesive strength of the discussed materials. MATERIAL AND METHODS: The tests were carried out on 2 groups of 40 bovine teeth in each group. In the 1st group, the classical orthophosphoric acid and the Transbond Plus self-etching primer (SEP) were used. In the 2nd group, the Transbond XT SEP was applied. In both groups, Transbond XT Light Cure Adhesive was used. The same metal orthodontic brackets were attached to the enamel surface. Optical Coherence Tomography (OCT) scans were made before and after removing brackets, which enabled tissue thickness measurements. The bond strength was evaluated using a universal testing machine. Parametric tests were performed on all obtained variables. Student's t-tests for independent samples and analysis of correlation with Pearson's r were carried out. RESULTS: The bond strength between the orthodontic bracket and enamel is statistically significantly different in the 1st group and the 2nd group, and is higher in the 2nd group. CONCLUSIONS: There are no significant differences in enamel thickness depending on the bonding system type and there is no correlation between the enamel thickness and the bond strength of orthodontic brackets to the enamel.
