Prediction of Eruption of Third Lower Molar in Panoramic Radiography Using Artificial Intelligence (AI): PDApp

Background: Preoperative prediction of mandibular third molar (M3) eruption remains a major challenge in dentistry and oral surgery. Artificial intelligence (AI) offers new opportunities to improve diagnostic accuracy and reduce the subjectivity associated with manual assessment. Objective: This study aimed to develop and validate PDApp, a machine-learning-based software designed to predict third lower molar eruption status (erupted vs. retained) using panoramic radiographic images, and to compare its diagnostic performance with clinical exploration. Materials and Methods: A retrospective dataset of 383 mandibular third molars with clinically confirmed eruption status was collected. Panoramic radiographic images were processed and used to train multiple machine learning (ML) algorithms integrated into PDApp for eruption prediction. The software’s performance metrics were analyzed and validated against clinical exploration, considered the diagnostic reference standard. Results: PDApp achieved the highest performance metrics among the evaluated ML approaches, reaching an accuracy of up to 99.5% in predicting mandibular third molar eruption. The tool showed strong reliability in differentiating erupted from retained M3 using panoramic radiographs. Conclusions: PDApp represents a robust, accurate, and easy-to-use AI-based tool for predicting third molar eruption potential in adolescent and teenage patients. Its implementation may enhance diagnostic efficiency, reduce common errors associated with manual evaluation, and support clinical decision-making in the management of impacted mandibular third molars. Future work will focus on integrating automatic calculation of radiographic ratios to achieve a fully automated prediction workflow.