Ontology and Bruxism: Do We Have Enough Information?

Area of Science:

• Dental Sleep Medicine
• Oral Health Research

Background:

• Current understanding of bruxism within dental communities requires improvement.
• Classifying bruxism based on ontological principles is currently speculative due to limited data.
• A need exists for standardized, non-preconceived methods for bruxism evaluation.

Purpose of the Study:

• To critically appraise the current efforts in defining and classifying bruxism.
• To highlight the importance of new data collection tools in advancing bruxism research.
• To discuss the potential of artificial intelligence in understanding bruxism's etiology, status, and consequences.

Main Methods:

• Review of current literature and expert panel discussions on bruxism classification.
• Introduction and consideration of the Standardized Tool for the Assessment of Bruxism (STAB) and BruxScreen.
• Exploration of potential applications of artificial intelligence in bruxism data analysis.

Main Results:

• Existing data is insufficient to establish definitive ontological classifications for bruxism.
• The development of STAB and BruxScreen represents a significant step towards standardized bruxism assessment.
• AI-driven data mining holds promise for developing predictive models for bruxism.

Conclusions:

• Ontological classification of bruxism is not currently supported by sufficient evidence.
• Standardized tools like STAB and BruxScreen are crucial for advancing bruxism research.
• Future research should focus on data collection and AI analysis to build evidence-based bruxism models.