AI-guided Prediction Model for Restoration Longevity using Intraoral Photographs and Occlusal Mapping

Abstract

The integration of artificial intelligence (AI) into restorative dentistry has created new opportunities for predicting restoration longevity through advanced digital assessment methods. This approach utilizes intraoral photographs, occlusal mapping, and machine learning algorithms to analyze restoration-related parameters, including surface deterioration, marginal integrity, occlusal stress distribution, and functional changes over time. AI-guided prediction models can process large volumes of clinical imaging data to identify early indicators of restoration failure and estimate the probability of long-term restoration survival. By combining two-dimensional photographic information with three-dimensional occlusal analysis, these systems provide a comprehensive evaluation of restoration performance and patient-specific risk factors. Such predictive frameworks may assist clinicians in developing personalized maintenance strategies, optimizing treatment planning, and reducing unnecessary restorative replacements. The application of computer vision, deep learning, and digital modeling technologies represents a shift toward preventive and data-driven restorative care. Although further clinical validation and standardized datasets are required, AI-based restoration longevity prediction models demonstrate significant potential to enhance accuracy, efficiency, and decision-making in modern dentistry.