AI-Driven Predictive Models for Early Detection of Periodontal Disease: A Data Management Approach in Clinical Dentistry

Abstract

Periodontal disease is a prevalent oral health condition that, if undiagnosed in its early stages, can lead to tooth loss, systemic complications, and increased healthcare costs. Traditional diagnostic methods clinical probing, radiographic evaluation, and periodontal charting are often subjective and limited in predictive capability. Recent advances in “artificial intelligence (AI)”, particularly predictive modeling and machine learning, provide new opportunities for early detection of periodontal disease by leveraging structured and unstructured clinical data. This study explores the development of AI-driven predictive models integrated with robust data management frameworks in clinical dentistry. Using datasets derived from “electronic dental records (EDRs)”, radiographs, and biomarker profiles, models such as logistic regression, random forests, and deep neural networks were trained to detect early signs of gingivitis and periodontitis. Results demonstrate that AI models achieved diagnostic accuracy exceeding 90% when supported by well-structured data pipelines and feature engineering. Moreover, predictive models identified high-risk patients before clinical symptoms became severe, offering opportunities for preventive interventions. Ethical considerations, including patient privacy, bias reduction, and transparency of predictive outputs, are also discussed. The study highlights AI as a transformative tool for predictive dentistry, enabling early diagnosis, personalized treatment, and improved oral healthcare outcomes