Evaluation of the effectiveness of artificial intelligence models in radiopaque and radiolucent lesions of the maxillofacial region on panoramic radiographs.

Abstract

Objectives: The aim of this study is to evaluate the success of algorithms used in deep learning (DL), a technique of artificial intelligence (AI), in the classification, detection, and segmentation of radiopaque, and radiolucent lesions in the maxillofacial region on panoramic radiographs (PR).

Methods: This study included PRs of individuals aged 12 to 80 years who presented with radiopaque or radiolucent findings in the maxillofacial region based on radiological examination. Lesions were classified on the dataset obtained from the PRs using AlexNet, VGG16, and GoogleNet architectures. The location detection and segmentation of lesions were performed using the YOLOv8 architecture. The classification, object detection, and segmentation performances of the DL architectures were evaluated.

Results: In the classification tasks using full PR, GoogleNet achieved the highest accuracy of 95.6%, with 97.1% precision and 95.5% F1 score in two-class lesion classification (lesion vs. no lesion). In distinguishing radiopaque and radiolucent lesions, VGG16 performed best, with 68.4% accuracy and 81.0% F1 score. For three-class and four-class classifications, GoogleNet again outperformed others with 61.6 and 75.7% accuracy, respectively. In cropped lesion-based classification, both GoogleNet and AlexNet achieved 96.5% accuracy. The YOLOv8m model demonstrated the best performance in object detection and segmentation, with 71.5% and 72.1% mean Average Precision (mAP), respectively.

Conclusion: These findings suggest that DL architectures, particularly GoogleNet for classification and YOLOv8m for object detection and segmentation, demonstrate strong potential in the automated analysis of maxillofacial lesions on panoramic radiographs. Their high performance in distinguishing lesion types and accurately localizing pathological areas indicates that such models could assist clinicians in early diagnosis and treatment planning, potentially reducing reliance on more complex imaging methods.