"Tooth GenAI:" Artificial intelligence integration for periodontal regeneration and dental treatment planning.

Background: Artificial intelligence (AI) has shown significant potential in improving diagnostic accuracy and treatment planning in dentistry. However, integrated systems combining image analysis and predictive modeling for periodontal prognosis remain limited. Materials and Methods: This study presents Tooth GenAI, an AI-based diagnostic framework integrating convolutional neural networks for caries detection and machine-learning regression models for the prediction of alveolar bone changes. A synthetic dataset of 10,000 patient records, including demographic and risk-factor variables, was generated. Five regression algorithms were evaluated. Results: Gradient Boosting Regressor demonstrated the best predictive performance for both 3-month and 6-month bone change prediction. The Convolutional Neural Network-based module achieved high accuracy in detecting carious lesions. The integrated system successfully combined image analysis and predictive modeling. Conclusion: Tooth GenAI demonstrates the feasibility of integrating AI-based image detection with predictive analytics to support periodontal diagnosis and treatment planning.