Enhancing AI-based oral decision support systems: Hybrid image processing for detecting impacted maxillary canines in orthopantomograms

Timely identification of impacted canines is crucial for preventing complications such as root resorption, misalignment, and damage to neighboring teeth. This research evaluates image processing techniques for AI-based oral decision support systems using orthopantomogram (OPG) images to determine effective preprocessing methods to improve deep learning (DL) model performance. The approach involved preprocessing OPG images followed by object detection using YOLOv5. Preprocessing techniques included median filtering, Gaussian blur, CLAHE, image sharpening, histogram stretching, and CLAHE combined with image sharpening. Performance was evaluated using precision, recall, F1 score, mAP, and IoU metrics. The CLAHE-sharpening combination achieved superior performance with precision of 0.934, recall of 0.932, F1 score of 0.931, mAP of 0.948, and IoU of 0.741, significantly outperforming unprocessed images. Grad-CAM visualizations confirmed that preprocessing enabled the model to identify relevant regions effectively. This study emphasizes the importance of preprocessing methods in improving the diagnostic accuracy in dental radiography for improved treatment planning. • Propose a hybrid preprocessing pipeline to improve AI-based detection of Impacted Maxillary Canines (IMCs) in orthopantomograms (OPGs). • Reveal how preprocessing consistently outperforms raw OPG input across all evaluation metrics. • Highlight the contribution of preprocessing to AI-based horizontal positioning in estimating the Treatment Difficulty Index. • Enhance Grad-CAM visualization through preprocessing to improve decision-making.