Affiliations: [a] Health Science Program, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand | [b] Tropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand | [c] Dental Department, Fang Hospital, Chiangmai, Thailand | [d] Department of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham, Thailand
Correspondence:
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Corresponding author: Choosak Nithikathkul, Tropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Thailand. E-mail: [email protected].
Abstract: BACKGROUND: Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency. OBJECTIVE: This review aims to evaluate the current and future trends in AI for diagnosing periodontitis. METHODS: A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included “artificial intelligence,” “panoramic radiograph,” “periodontitis,” “periodontal disease,” and “diagnosis.” RESULTS: The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency. CONCLUSIONS: Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.
