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Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.
The main objective of this study was to test whether subjects with different degrees of bruxism differ regarding EMG parameters and whether CES intervention affects those parameters. The hypothesis was that CES influences EMG parameters and after its’ cessation, all EMG parameters return to baseline (exposure–response relationship).
For this purpose, forty subjects were examined, 16 men and 24 women, matched for age and gender and assigned randomly in the intervention (N=20) and control group (N=20). The procedure was as follows: 1-week inactive GC (N=40), 2 weeks inactive/active GC (N=20/N=20), 2 weeks inactive GC (N=40). Each interval was followed by a surface EMG recording from eight muscle parts (right and left anterior -, medial -, and posterior masseter and right and left anterior temporalis) under force-controlled feedback (BiteFork®) with three submaximal bite forces. The resulting EMG activity is expressed as RMS % MVC and RMS at MVC. The statistics is performed with t-test, one-way rmANOVA, and Friedman rmANOVA on ranks, according to the distribution of the data. The significance level was set at p≤0.05.
The results generated from the within-groups and between-groups comparison were mostly not statistically significant and could therefore not offer clinically relevant conclu-sions.
However, it cannot be excluded that a higher submaximal bite force and an extended intervention interval would have rendered different outcomes. The insufficient study sample resulted in a low observed power which makes the findings prone to Type II er-ror. It can be concluded that this study did not find any substantiating differences be-tween the EMG values of participants with various bruxism activity and that CES could not influence the studied EMG parameters in the two weeks intervention time.
Our hypothesis which supposes that subjects with high and low bruxism activity differ in RMS % MVC could not be verified. However, with the gained knowledge, it is recom-mended to further elaborate a definite bruxism diagnosis by using portable EMG devices.