A wearable self-learning expert system for dental trauma diagnosis process

Abstract

This study proposes a novel intelligent system for the diagnosis of dental trauma. The system utilizes the International Dental Trauma Association's (IADT) established guidelines as its knowledge base and employs a data-driven, forward-chaining inference engine to identify the specific injury type. Following diagnosis, dentists can leverage the system to generate comprehensive reports detailing the entire diagnostic process and meticulously plan subsequent treatment procedures. Additionally, the system facilitates the secure storage of patient data, enabling potential integration with existing hospital information systems. Designed for deployment on both smart glasses and mobile devices, this application empowers dental professionals with a readily accessible, wearable diagnostic tool grounded in the latest IADT recommendations. The system leverages forward-chaining reasoning to diagnose a broad spectrum of dental trauma cases, encompassing both uncomplicated and complex presentations. Furthermore, it generates reports that not only outline the diagnostic conclusion but also transparently document the decision-making process alongside relevant patient information. This innovative application offers significant enhancements to the conventional diagnostic workflow by facilitating uninterrupted eye contact with the patient during examinations. As a result, it is anticipated to provide valuable support to specialists, general dentists, and even dental students in effectively managing the diagnosis, treatment planning, and follow-up of dental trauma cases.

Keywords

• Expert systems
• neural networks
• dental trauma
• wearable devices

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