Background/Objective: Preoperative prediction of third molar eruption with artificial intelligence (AI) has been a challenge in dentistry and oral surgery. Methods: In this investigation, we used machine learning (ML) algorithms to characterize M3 panoramic radiological images for the preoperative differential diagnosis of third nolar eruption/retention and compared them with clibical explorations to validate their performance. Results: We retrospectively collected data of patients with mandibular thid molar retention, where all eruption diagnoses were confirmed via clinical exploration. A total of 383 panoramic radiographies were selected to train the PDApp software for eruption diagnosis for the software validation. Conclusions: The PDApp software achieved the highest performance metrics for the prediction of mandibular third molar eruption
Santeiro-Hermida et al. (Mon,) studied this question.