Intelligent Occlusion Stabilization Splint with Stress-Sensor System for Bruxism Diagnosis and Treatment

Abstract

Bruxism is a masticatory muscle activity characterized by high prevalence, widespread complications, and serious consequences but without specific guidelines for its diagnosis and treatment. Although occlusal force-based biofeedback therapy is proven to be safe, effective, and with few side effects in improving bruxism, its mechanism and key technologies remain unclear. The purpose of this study was to research a real-time, quantitative, intelligent, and precise force-based biofeedback detection device based on artificial intelligence (AI) algorithms for the diagnosis and treatment of bruxism. Stress sensors were integrated and embedded into a resin-based occlusion stabilization splint by using a layering technique (sandwich method). The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm was leveraged for occlusal force data processing and parameter configuration. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent splint. Experiment results showed reasonable parameter metrics for the sensors system and demonstrated the feasibility of the proposed scheme for bruxism treatment. The intelligent occlusion stabilization splint with a stress sensor system is a promising approach to bruxism diagnosis and treatment.Sensors 2020, 20, 89 2 of 15 discussion summary, the existing assessment of bruxism could also be classified into three main aspects: (1) noninstrumental approaches, (2) instrumental approaches, and (3) cut-off points grading [6]. Self-report and clinical examination are considered as noninstrumental approaches, which are also the primary choices in the clinical assessment of bruxism. However, their reliability and validity need further improvement [6]. EMG may provide good evidence of both sleep and awake bruxism, but there is also a risk of overestimating the number of true SB events [7]. PSG could be regarded as a reference standard for SB assessment; however, it is expensive and time-consuming [2]. To date, the reliability and validity of all the common techniques remain debatable, and consensus has yet to be established regarding the best method to diagnose bruxism. Therefore, exploring some new methods of bruxism diagnosis and management is a necessary and meaningful research topic.The use of biofeedback technologies (electrical, auditory, vibratory stimulus, etc.) as behavioral techniques of bruxism diagnosis and treatment has been considered a promising approach in both clinical and scientific fields in recent years [8]. Various biofeedback modalities have been reported in previous papers, and most of these are based on EMG recordings, except two studies that described force-based devices [8,9]. An intra-splint force detector (ISFD) for SB force detection was described in 2003 [10]. A detailed description of this SB inhibition system has been presented in a recently published paper [11]. This inhibition system consisted of ISFD, vibration, and co...

Bonding Interface and Repairability of 3D-Printed Intraoral Splints: Shear Bond Strength to Current Polymers, with and without Ageing

“…Moreover, a digitally fabricated splint can protect surgical areas that were treated with stem cells in guided bone regeneration within periimplantitis or jaw augmentation; and might need adjustment during healing process [ 54 ]. The secure addition (bonding) of resins to 3D printed appliances may also enable the later integration of sensors, in order to measure biting forces in occlusal splints or insertion torque of implants in drill guides [ 55 , 56 ].…”

Sandwich Integration Technique for the Pressure Sensor Detection of Occlusal Force In Vitro

“…The main body of this device consisted of three layers: upper, intermediate, and lower, of which the upper and lower layers fabricated with dental resin materials, whereas the intermediate layer was built up with both resin and a piezoresistive-film sensor. The sandwich technique was outlined in a previously published article [ 23 ]. Meanwhile, future exploration and improvements have been conducted based on the foundation of our preliminary research.…”

“…Lantada et al designed a bite force sensor for monitoring bruxism based on magnetic near-field communication [ 22 ]. The inputted bite force signals were then converted into digital signals and transmitted by the wireless Bluetooth to a server terminal [ 23 ]. These collected bite force data can be analyzed by deep learning, which is the representative virtual component of AI.…”

Detection of EMG Signals by Neural Networks Using Autoregression and Wavelet Entropy for Bruxism Diagnosis

“…In experiments on patients, it was found that 22.5 kg average force of duration 7.1 s can be generated 3.6 times in one hour [7]. Moreover, occlusal forces during bruxism can be detected by an artificial intelligence algorithm via strain gauge pressure sensors placed inside the mouth [8].…”