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

OBJECTIVES

This study aims to develop and validate an artificial intelligence (AI) - driven model to assist periodontal therapy decision-making and minimize tooth loss.

METHODS

A retrospective longitudinal cohort study was conducted using clinical and radiographic data from 3347 teeth treated and followed up for at least 10 years. The parameters included in the machine learning training and testing processes included: probing pocket depth (PPD), bone loss (BL), systemic diseases, therapy type, and others. Various machine learning models were developed and evaluated for accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC).

RESULTS

The Random Forest model demonstrated superior performance and was selected as the final predictive model achieving an AUC score of 0.91 and an accuracy of 0.93. Significant associations were found between tooth loss and variables such as age, PPD, bone loss, and furcation involvement.

CONCLUSION

This AI-driven platform may provide a reliable tool for stratifying periodontal therapy decisions and predicting tooth loss risk, offering clinicians a supportive approach to personalize treatment plans. However, the study's retrospective design and reliance on traditional clinical metrics highlight the need for future prospective studies.

CLINICAL SIGNIFICANCE

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.