## 4 Discussion
Furthermore, it can streamline the process of orthodontic treatment and prosthodonic interventions by providing precise measurements and the localization of dental structures. The proposed approach, based on deep learning techniques, demonstrates high accuracy and effectiveness. [PERSON] et al. [33] presented an approach for automated teeth segmentation in dental panoramic X-ray images using a multimodal convolutional neural network (CNN) architecture. The authors propose a multimodal CNN architecture that combines information from multiple modalities for teeth segmentation. They utilize both the original grayscale X-ray images and their corresponding edge maps as input to the network. The edge maps were generated using an edge detection algorithm to capture the shape and boundaries of the teeth. The multimodal CNN architecture was designed to leverage both the intensity information and structural characteristics provided by the original images and edge maps, respectively. The peculiarity of this study is the use of three different CNN-based architectures, i.e., conventional CNN, atrous CNN, and separable CNN, which were tested on 1500 panoramic images. The authors trained the multimodal CNN using a combination of binary cross-entropy loss and Dice coefficient loss, which encourages accurate segmentation. The performance of the proposed method is evaluated using metrics such as accuracy, sensitivity, specificity, and the Dice similarity coefficient. The proposed method achieves high accuracy, sensitivity, specificity, and DSC, indicating accurate segmentation and minimal false positives and negatives, with precision and recall of 95.01% and 94.06%, respectively. A comparison with other methods shows superior performance, highlighting the advantages of incorporating both intensity and structural information through the multimodal approach. Other authors [34] used forty dental images that were manually annotated to create the ground truth to train two CNNs: U-net and Faster RCNN. These models were trained and validated on a dataset including 40 OPTs. The CNN was trained to segment individual teeth and assign correct numbering to them based on dental conventions. The AI model employs the CNN architecture to perform teeth segmentation on OPG images. The network learns to identify and delineate the boundaries of individual teeth by analyzing patterns and textures. The training process involves iterative optimization using annotated OPG images, where each tooth is manually labelled. The model learns to accurately segment teeth by leveraging the patterns and similarities shared among different OPG images. Once the teeth segmentation is accomplished, the AI model assigns correct numbering to the segmented teeth based on established dental conventions. The model incorporatesdental knowledge and algorithms to determine the appropriate numbering scheme for each tooth. The numbering process considers factors such as tooth position, dental arch, and universal tooth numbering system. The performance of the proposed AI model was evaluated using a dataset of OPG images, both in terms of teeth segmentation accuracy (precision = 88.8%, accuracy = 88.2%, recall = 87.3%, F-1 score = 88%, Dice index = 92.3%, and IoU = 86.3%) and correct numbering assignment. The results demonstrated high accuracy and consistency in teeth segmentation, achieving a significant overlap with manual segmentations. The assigned tooth numbering was also compared with expert annotations, showing accurate and reliable numbering in line with dental conventions. A recent article reported the development of an advanced deep-learning approach to improve the accuracy and efficiency of tooth segmentation in OPT [35]. The authors present the \"Teeth U-Net\" model based on the U-Net architecture that was specifically designed to handle dental panoramic X-ray images and addresses the challenges associated with tooth segmentation, including variations in tooth shapes, overlapping structures, and image quality issues.