The convergence of digital orthodontics and periodontology has ushered in a new era of precision dentistry, where data-driven approaches, particularly artificial intelligence (AI) and machine learning (ML), play a pivotal role in risk assessment. Orthodontic treatment, while effective for correcting malocclusions, can pose significant risks to periodontal health, including gingival inflammation, alveolar bone loss, and increased susceptibility to periodontitis. Traditional assessment methods rely on clinical judgment and manual measurements, which are subjective and time-consuming. In contrast, data-driven methods leverage large datasets from digital imaging, electronic health records, and biosensor data to predict, monitor, and mitigate these risks with greater accuracy and efficiency. This narrative review synthesizes peer-reviewed literature published to explore the integration of digital tools in orthodontics and periodontology, focusing on data-driven risk assessment strategies. We examine the evolution of digital technologies such as intraoral scanners, cone-beam computed tomography (CBCT), and 3D modeling, and how they facilitate the collection of high-dimensional data for AI applications. Key themes include the use of ML algorithms for predicting periodontal deterioration during orthodontic therapy, automated detection of gingival changes, and personalized risk profiling. The review highlights clinical applications, such as AI-assisted cephalometric analysis and volumetric gingival assessment, which enhance diagnostic precision and treatment outcomes.
Despite promising advancements, challenges such as data privacy, model bias, and the need for interdisciplinary validation persist. Overall, data-driven approaches hold transformative potential for improving patient safety and efficacy at the ortho-perio interface, paving the way for predictive, personalized care in dentistry.
