Jundaeng Jarupat, Chamchong Rapeeporn, Nithikathkul Choosak
Health Science Program, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand.
Tropical Health Innovation Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand.
Technol Health Care. 2025;33(1):473-484. doi: 10.3233/THC-241169.
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.
This review aims to evaluate the current and future trends in AI for diagnosing periodontitis.
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."
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.
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.
人工智能（AI）是牙科牙周炎诊断领域的先进技术。当前的诊断挑战包括因缺乏经验丰富的牙医、X线片分析时间有限以及强制报告而导致的错误，这会影响护理质量、成本和效率。
本综述旨在评估人工智能在牙周炎诊断方面的当前及未来趋势。
按照PRISMA指南进行了全面的文献综述。我们在包括PubMed、Scopus、Wiley Online Library和ScienceDirect在内的数据库中搜索了2018年1月至2023年12月发表的研究。搜索中使用的关键词包括“人工智能”“全景X线片”“牙周炎”“牙周疾病”和“诊断”。
该综述从最初的211条记录中纳入了12项研究。这些研究使用了先进的模型，特别是卷积神经网络（CNN），牙周骨丧失检测的准确率在0.76至0.98之间。方法包括深度学习混合方法、自动识别系统和机器学习分类器，提高了诊断的准确性和效率。
将人工智能创新整合到牙周炎诊断中可提高诊断的准确性和效率，为传统方法提供了有力的替代方案。这些技术为经典方法提供了更快、劳动强度更低且更精确的替代方法。未来的研究应专注于提高人工智能模型的可靠性和通用性，以确保其在临床中广泛应用。
