Diagnosis and management of bruxism: Evaluation of clinical practices in France

Attitude of Italian dental practitioners toward bruxism assessment and management: A survey-based study

INTRODUCTION

Bruxism is a spectrum of masticatory muscles activities. According to the recent evidence and international consensus, there is no indication to treat bruxism unless clinical consequences are present. The aim of the present study was to investigate the approach of Italian dentists to bruxism in their clinical practice.

METHODS

An anonymous online survey was developed, composed by 26 items grouped into five sections: (1) Characteristics of the respondent (Q1-Q6); (2) Bruxism frequencies (Q7-Q11); (3) Bruxism assessment (Q12-Q15); (4) Bruxism management (Q16-Q20); (5) Occlusal splints therapy (Q21-Q26).

RESULTS

A total of 384 practitioners, including specialists and non-specialists, participated in this survey. The majority of the respondents reported that 1-3 out of ten patients presented with bruxism. The diagnosis is mainly performed with non-instrumental methods, and facial pain and dental wear are the major clinical consequence associated with bruxism. Almost the entire sample of respondents report to offer occlusal splints to bruxism patients, and 41% refer to perform occlusal adjustments. Almost half of the respondents have never proposed any form of cognitive-behavioural therapy.

CONCLUSION

The results of the current survey highlighted several inconsistencies in Italian dentists' approaches to bruxism, suggesting the need for better education of practitioners and for the development of a standardized protocol to assess and manage bruxism in dental practices.

Lingual treatment of an adult patient with an iatrogenic class II anterior open bite: Report of a case with a non-surgical orthodontic approach

INTRODUCTION

The treatment of adult patients presenting an open bite is a real challenge for orthodontists due to the complexity of the malocclusion. The diagnosis is essential to identify aetiological factors and establish the best aesthetic and functional therapeutic prognosis. Among the aetiological factors, iatrogenic treatment history has to be considered. It is the case here where a regrettable unsuitable splint for bruxism treatment forced an open bite to appear.

MATERIAL AND METHOD

This case report presents an adult women with a large open bite due to an occlusal splint not covering the last mandibular molars. It was not really a vertical skeletal excess but more a mandibular clockwise rotation linked to the molar extrusions, which created a dento-alveolar open bite and labio-mental contractions during lip closure. Treatment objectives were the intrusion of the third and second molars, the extrusion of incisors to obtain a counter clockwise mandibular rotation. The patient was treated with an aesthetic and customized lingual appliance with no orthognathic surgery. A physiotherapy was followed by the patient when the open bite was closed in order to facilitate and stabilize the intercuspation.

RESULTS

A complete closing of the open bite was reached after two years of treatment. The superimposition showed the anticlockwise mandibular rotation. The levelling with posterior intrusion and the incisors extrusion reduced the vertical dimension with success. After 3years of retention, the occlusal stability was obtained.

CONCLUSION

The intrusion of the molars associated with a counter clockwise mandibular rotation made it possible the incisor extrusion. The occlusal balance with physiotherapy and patient compliance maintained the closure. In this way, the iatrogenic effects of the poorly designed bite splint were repaired.

Electromyographic Patterns and the Identification of Subtypes of Awake Bruxism

The future of awake bruxism assessment will incorporate physiological data, possibly electromyography (EMG) of the temporal muscles. But up to now, temporal muscle contraction patterns in awake bruxism have not been characterized to demonstrate clinical utility. The present study aimed to perform surface EMG evaluations of people assessed for awake bruxism to identify possible different subtypes. A 2-year active search for people with awake bruxism in three regions of the country resulted in a total of 303 participants (223 women, 38 ± 13 years, mean and SD). Their inclusion was confirmed through non-instrumental approaches for awake bruxism: self-reported questionnaire and clinical exam, performed by three experienced and calibrated dentists (Kappa = 0.75). Also, 77 age- and sex-matched healthy controls were recruited (49 women, 36 ± 14 years). Temporalis surface EMG was performed with a portable device (Myobox; NeuroUp, Brazil). EMG signals were sent to a computer via Bluetooth 4.0 at a sampling rate of 1,000 Hz. Digital signal processing was performed using the commercial neuroUP software, transformed in RMS and then normalized for peak detection (EMG peaks/min), in a 10 min session. Cluster analysis revealed three distinct subtypes of awake bruxism: phasic, tonic, and intermediate. Individuals with a predominance of EMG peaks/min were classified as the "phasic" subtype (16.8%). Those with the highest EMG rest power were classified as the "tonic" subtype (32.3%). There was also an "intermediate" subtype (50.8%), when both variables remained low. Characterization of awake bruxism physiology is important for future establishment of instrumental assessment protocols and treatment strategies.

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.