Sarakbi Raafat Musief, Varma Sudhir Rama, Muthiah Annamma Lovely, Sivaswamy Vinay
Department of Clinical Sciences, Ajman University, Ajman, United Arab Emirates.
Center for Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
Front Oral Health. 2025 May 14;6:1561128. doi: 10.3389/froh.2025.1561128. eCollection 2025.
Gingivitis and periodontitis, are widespread conditions with diverse influence on oral and systemic health. Traditional diagnostic methods in periodontology often rely on subjective clinical assessments, which can lead to variability and inconsistencies in care. Imbibing artificial intelligence (AI) facilitates a significant solution by enhancing precision metrics, treatment planning, and personalized care. Studies published between 2018 and 2024 was conducted to evaluate AI applications in periodontal maintenance. Databases such as PubMed, Cochrane, Web of Science and Scopus were searched using keywords like "artificial intelligence," "machine learning," and "periodontitis." Studies employing AI for diagnosis, prognosis, or periodontal maintenance using clinical or radiographic data were included. Deep learning algorithms such as convolutional neural networks (CNNs) and segmentation techniques were analyzed for their diagnostic accuracy. AI demonstrated superior performance in detecting periodontal conditions, with accuracy rates surpassing 90% in some studies. Advanced models, such as Multi-Label U-Net, exhibited high precision in radiographic analyses, outperforming traditional methods. Additionally, AI facilitated predictive analytics for disease progression and personalized treatment strategies. AI has transformed periodontal care, offering accuracy, personalized care, and efficient workflow integration. Addressing challenges like standardization and ethical concerns is critical for its broader adoption.
牙龈炎和牙周炎是常见病症，对口腔和全身健康有多种影响。牙周病学中的传统诊断方法通常依赖主观临床评估，这可能导致治疗的变异性和不一致性。引入人工智能（AI）通过提高精准度指标、治疗规划和个性化护理，提供了一个重要的解决方案。开展了2018年至2024年间发表的研究，以评估AI在牙周维护中的应用。使用“人工智能”“机器学习”和“牙周炎”等关键词搜索了PubMed、Cochrane、科学网和Scopus等数据库。纳入了使用AI通过临床或放射学数据进行诊断、预后或牙周维护的研究。分析了卷积神经网络（CNN）等深度学习算法和分割技术的诊断准确性。AI在检测牙周病症方面表现出卓越性能，在一些研究中准确率超过90%。先进模型，如多标签U-Net，在放射学分析中表现出高精度，优于传统方法。此外，AI有助于对疾病进展进行预测分析和制定个性化治疗策略。AI已经改变了牙周护理，提供了准确性、个性化护理和高效的工作流程整合。应对标准化和伦理问题等挑战对其更广泛应用至关重要。
