A Novel Artificial Intelligence Approach to Kennedy Classification for Partially Edentulous Patients Using Panoramic Radiographs.

OBJECTIVES

This study aimed to develop an artificial intelligence system for automated classification of partially edentulous arches from panoramic radiographs using the Kennedy classification system and Applegate's rules, alongside identifying existing teeth for automated reporting.

METHODS

From 5261 anonymized digital panoramic radiographs collected from publicly available datasets, 1875 high-quality images were selected and divided into training (80%), validation (10%), and testing (10%) sets. Teeth were manually annotated on the Roboflow platform following the Universal Numbering System. To enhance model robustness, data augmentation techniques were applied, expanding the dataset to 2398 images. For tooth detection, a YOLOv8s deep learning model was trained for 80 epochs (batch size: 16, learning rate: 0.01). Performance was evaluated using precision, recall, F1 score, and mean average precision. Detected teeth were used to classify partially edentulous areas based on the Kennedy system. Modification areas were identified by analyzing detected and missing teeth, measuring bounded distances in millimetres, and classifying free-end saddle gaps.

RESULTS

The YOLOv8s model achieved a mean average precision (mAP50) of 98.1% for tooth identification, with precision and recall of 95.7% and 95.8%, respectively. For Kennedy classification, the model demonstrated precision of 0.962, recall of 0.931, and an F1-score of 0.939 across maxillary and mandibular arches.

CONCLUSIONS

The high accuracy and efficiency of this AI-driven approach can standardize classification, reduce diagnostic variability, and alleviate the workload for dental professionals, enabling seamless integration into clinical practice.

CLINICAL RELEVANCE

This AI system provides a consistent, accurate, and reliable method for classifying partially edentulous arches from panoramic radiographs, reducing manual assessment variability, easing practitioner workload, and enabling large-scale analysis of partial edentulism prevalence.