Enhancing Early Detection of Gingivitis and Periodontitis: A Web-Based Expert System Utilizing Certainty Factor Analysis - Kritsada, Chuthan; Somsak, Srisuk; Srisuwan, Pichai; Anong, Buranakorn (Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE), 2023)
Periodontal diseases, including gingivitis and periodontitis, are prevalent global oral health issues caused by bacterial infections that damage the tissues supporting teeth, potentially leading to tooth loss. Despite their high incidence, many individuals delay seeking treatment until the diseases reach advanced stages, exacerbating complications and systemic health risks. To address this issue, this research proposes an expert system for diagnosing periodontal diseases utilizing the Certainty Factor (CF) method. The CF method, a branch of artificial intelligence, handles uncertainty in medical diagnostics by combining expert knowledge with patient-reported symptoms to estimate the probability of specific diagnoses. Implemented as a web-based application using ASP.NET and C#, the system provides users with real-time diagnostic feedback and treatment recommendations. Tested with patient data from a dental clinic in Thailand, the system demonstrated a 98% accuracy rate in diagnosing gingivitis and periodontitis. This approach not only facilitates early diagnosis but also reduces the burden on healthcare systems, especially in areas with limited dental care access. The findings underscore the effectiveness of integrating AI-driven expert systems in enhancing public health outcomes and improving accessibility to dental care.