Development of an AI-Supported Clinical Tool for Assessing Mandibular Third Molar Tooth Extraction Difficulty Using Panoramic Radiographs and YOLO11 Sub-Models

<b>Background/Objective:</b> This study aimed to develop an AI-supported clinical tool to evaluate the difficulty of mandibular third molar extractions based on panoramic radiographs. <b>Methods:</b> A dataset of 2000 panoramic radiographs collected between 2023 and 2024 was annotated by an oral radiologist using bounding boxes. YOLO11 sub-models were trained and tested for three basic scenarios according to the Pederson Index criteria, taking into account Winter (angulation) and Pell and Gregory (ramus relationship and depth). For each scenario, the YOLO11 sub-models were trained using 80% of the data for training, 10% for validation, and 10% for testing. Model performance was assessed using precision, recall, F1 score, and mean Average Precision (mAP) metrics, and different graphs. <b>Results:</b> YOLO11 sub-models (nano, small, medium, large, extra-large) showed high accuracy and similar behavior in all scenarios. For the calculation of the Pederson index, nano for Winter (average training [email protected] = 0.963; testing [email protected] = 0.975), nano for class (average training [email protected] = 0.979; testing [email protected] = 0.965), and medium for level (average training [email protected] = 0.977; testing [email protected] = 0.989) from the Pell and Gregory categories were selected as optimal sub-models. Three scenarios were run consecutively on panoramic images, and slightly difficult, moderately difficult, and very difficult Pederson indexes were obtained according to the scores. The results were evaluated by an oral radiologist, and the AI system performed successfully in terms of Pederson index determination with 97.00% precision, 94.55% recall, and 95.76% F1 score. <b>Conclusions:</b> The YOLO11-supported clinical tool demonstrated high accuracy and reliability in assessing mandibular third molar extraction difficulty on panoramic radiographs. These models were integrated into a GUI for clinical use, offering dentists a simple tool for estimating extraction difficulty, and improving decision-making and patient management.