AHAMEDHA KAMARUL FENANA SHAHIR

Periodontitis is a multifactorial inflammatory disease characterized by progressive destruction of the periodontal supporting tissues, resulting from complex interactions between microbial biofilm and host immune responses. Conventional diagnostic approaches, including periodontal probing and radiographic evaluation, are limited by examiner variability and challenges in interpreting multiple interacting risk factors. In recent years, Artificial Intelligence (AI) has emerged as a promising adjunct in periodontology, enhancing diagnostic precision, risk assessment, and personalized treatment planning. AI applications in clinical periodontology include Natural Language Processing (NLP) for structured data extraction and improved clinical documentation, as well as machine learning and deep learning models for radiographic and clinical analysis. Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) have demonstrated high accuracy in detecting periodontal bone loss, classifying disease severity, identifying implant systems, and predicting disease progression. Integration of radiographic, clinical, and multi-omics datasets further supports comprehensive risk profiling and precision-based care. Additionally, AI-assisted biomarker analysis using saliva and gingival crevicular fluid shows potential for non-invasive early detection. Emerging technologies such as smartphone-based monitoring systems, AI-enabled oral hygiene devices, and augmented/virtual reality–based educational tools enhance patient engagement and professional training. Despite challenges including data privacy concerns, ethical considerations, high implementation costs, and limited large-scale clinical validation, AI represents a valuable assistive technology that strengthens clinical decision-making and advances personalized periodontal care. This article aims to comprehensively discuss the current applications of artificial intelligence in periodontology, highlighting recent advancements, clinical implications, limitations, and future perspectives for integrating AI into routine periodontal practice. Keywords: Periodontitis; Artificial Intelligence; Deep Learning; Convolutional Neural Networks; Artificial Neural Networks; Natural Language Processing; Periodontal Diagnosis; Radiographic Analysis; Biomarkers;