Development of an artificial intelligence model for assisting periodontal therapy decision-making: A retrospective longitudinal cohort study

This study introduces and validates a novel AI-driven predictive model for periodontal therapy, utilizing data from treatment cases. Unlike previous models, this approach integrates multiple clinical and radiographic parameters, demonstrating high predictive accuracy (AUC=0.91, accuracy=0.93). The use of the Random Forest algorithm allows for robust predictions, offering an innovative, data-driven approach to periodontal treatment planning. Implementing AI in periodontal therapy decision-making may have the potential to improve patient outcomes by guiding clinicians toward optimal treatment strategies, enhancing therapeutic precision, and reducing the likelihood of unnecessary interventions.