In contemporary society, dental diseases affect people of all ages, increasing the workload of dentists. Oral panoramic imaging is a widely used diagnostic tool in dentistry, and doctors must process image data from numerous patients amid their heavy daily clinical workload. However, manually analyzing complex image data is time-consuming, laborious, and susceptible to various human factors, such as fatigue, emotional fluctuations, and differences in professional skills. These factors can adversely affect diagnostic accuracy, delay treatment, and damage patient health. Although artificial intelligence (AI) is initially applied in dental disease detection, most current AI research focuses on single disease or restoration. However, when the number of detection targets increases, the decrease in detection accuracy can hinder practical clinical applications. Therefore, this study applies deep learning to identify key image features for efficient and accurate lesion screening in oral panoramic images using a deep learning network architecture. The purpose is to detect abnormal teeth and restorations including dental cavities, blocked teeth, implants, root canal treated teeth, fillings, crowns, and bridges. Intelligent assistance methods can be used to reduce human errors, accelerate diagnoses, and improve medical quality and efficiency.

This study proposes an intelligent assisted diagnostic network based on the YOLOv8 framework, designing a YOLOv8 model specifically for dental imaging. The purpose is to detect abnormal teeth and restorations including cavities, residual teeth, implants, root canal treated teeth, fillings, crowns, and bridges. Intelligent assistance methods can be used to reduce human error, accelerate diagnosis, and improve medical quality and efficiency. First, to enhance feature extraction capability, we integrated a spatial grouping enhancement (SGE) attention mechanism to enhance the model ability to capture complex oral features. In addition, to address the difficulty of identifying small lesions, a small-object detection layer was added. This layer integrates multiple features and maintains detailed information, thereby enhancing the capability of the model in detecting fine lesions. Subsequently, the model loss function was optimized, adopting the generalized intersection over union (GIoU) loss function to improve the prediction accuracy of bounding box, which further enhanced localization performance. Finally, to reduce the computational burden of improved model, the layer-adaptive magnitude-based pruning (LAMP) method was used. This method eliminates non-contributing channels and improves detection speed.

The analysis in Table 2 shows that the SGE attention mechanism performs well in target recognition, outperforming other attention mechanisms in all detection results. Table 4 shows the results of the ablation experiment, indicating that integrating the SGE attention mechanism into the baseline model improves accuracy, recall, and mean average precision (mAP) by 2.4, 2.6, and 1.0 percentage points, respectively. This indicates that the SGE attention mechanism can effectively group features, improve recognition rate, enhance feature extraction, and suppress information interference. After the addition of small-object detection layer, accuracy, recall, and mAP increased by 3.0, 2.4, and 2.1 percentage points, respectively, indicating that the small-object detection layer effectively identifies smaller detection targets and enhances the network ability to recognize small objects. After replacing completing intersection over union (CIoU) with GIoU, the accuracy and mAP increased by 3.6 and 1.2 percentage points, respectively; however, the recall rate decreased by 0.7 percentage points. This indicates that GIoU enhances localization performance and improves recognition accuracy. The final model, YOLOv8-Dental, was developed using the LAMP method, which improved accuracy, recall, and mAP by 5.2, 4.6, and 5.2 percentage points, respectively, while reducing parameters and computational complexity by 2.02×10⁶ and 0.9×10⁹, respectively. Table 5 shows the comparative experiments, indicating that although YOLOv8-Dental performed slightly worse than some models in terms of implants and dental bridges, it still achieved recognition rates of 95.1% and 96.2% for these, respectively. In detecting the remaining five lesions, the proposed model outperformed the other models in average precision (AP) with fewer parameters and a lower computational workload. This ensures high detection accuracy for multiple lesions and maintains the overall detection rate.

This study explored the deep learning-based AI-assisted diagnosis of dental panoramas, aiming to reduce the healthcare burden of dentistry, assist dentists beyond the limitations of subjective judgment, and improve diagnostic accuracy. First, YOLOv8 was used as the base network, which was enhanced by integrating the SGE attention mechanism into its backbone feature extraction network. Second, to detect small target lesions in oral images, a small target detection layer was added to improve recognition accuracy. To further enhance the model bounding box localization accuracy, the GIoU loss function was adopted, which significantly improved the network bounding box regression performance. Finally, the model was pruned using the LAMP method to reduce the number of parameters and computation, thereby improving detection speed. All these optimization strategies were integrated to build the YOLOv8-Dental-assisted diagnosis model. Comparisons and ablation experiments demonstrated the positive impact of each optimization strategy on the diagnosis model. The experimental results showed that the YOLOv8-Dental model achieved a precision rate of 83.9%, recall rate of 87.8%, mAP of 89.8%, and frame rate of 409 frame/s for detecting cavities, blocked teeth, implants, root canal treated teeth, fillings, crowns, and bridges. The validity of the model was verified through physical image detection and heatmap analysis. The results of this study provide theoretical guidance and a methodological reference for deep-learning-based clinical diagnosis, promoting the research on deep-learning-based image-assisted diagnosis of dental diseases.