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.
Highlights
Bruxism is one of chronic dental problems worldwide with multifactorial etiology and no golden standard for diagnosis and treatment [1]
We show the experiment results with the sandwich method and the electrical measurements of the sensors; we demonstrate the
In2019, thisxx, section, we present the prototype of our sensor system
Summary
Bruxism is one of chronic dental problems worldwide with multifactorial etiology and no golden standard for diagnosis and treatment [1]. The disorder is defined as a repetitive jaw muscle activity characterized by clenching or grinding of teeth and/or by bracing or thrusting of the mandible [2]. Previous investigations found that the prevalence of sleep bruxism (SB) was about 50% in adults [3]. Whilst the prevalence of SB in children ranged from 3.5% to 40.6% [4]. Signs and symptoms of bruxism vary, it is always supposed to be an etiological factor in causing damage to supporting structures of teeth, abnormal tooth wear, failure of dental restorations, and temporomandibular and musculoskeletal disorders [5].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
