## 4 Discussion  
The findings from this study emphasize the potential of the CNN model as a robust tool for predicting the need for dental SMs based on X-ray imagery. The model demonstrated a high overall accuracy of 94%, supported by other performance metrics such as precision, recall, F1-score, and ROC AUC. These results indicate that the model can effectively support pediatric dentists by automating the assessment of dental X-rays, minimizing diagnostic delays, and enhancing treatment planning. Below, we discuss the insights derived from these results, compare them with findings in similar studies, and identify limitations and opportunities for future research. Figure 6: Prediction result indicating no need for a space maintainer after the extraction of teeth 75 and 85. Figure 7: Prediction result indicating the necessity of a space maintainer after the extraction of tooth 75. The results show the CNN model's ability to generalize effectively, as evidenced by its low false positive and false negative rates. High precision for Class 0 (0.93) indicates the model's success in avoiding unnecessary interventions, which is critical in pediatric dentistry. Similarly, the recall for Class 1 (0.93) underscores the model's ability to identify all cases requiring an SM, ensuring no critical cases are overlooked. Visual examples, such as Figures 6 and 7, highlight the practical application of the model. The accurate identification of structural dental anomalies (e.g., spacing issues) demonstrates the model's capability to process and analyze complex patterns in X-ray images. Furthermore, the model's ability to pinpoint specific tooth numbers adds a layer of precision that could streamline clinical workflows and decision-making processes. The ROC AUC score of 0.94 obtained in this study is consistent with the results reported in other dental AI applications, such as [34], where CNN models were applied to classify orthodontic issues or identify dental caries. The confusion matrix and classification report highlight the model's ability to maintain a balance between precision and recall, like the results observed in studies like [41], where CNNs were used to predict the need for orthodontic retainers. These comparisons affirm that the model's performance is not only competitive but also consistent with the capabilities of state-of-the-art AI systems in dental diagnostics. The focus on tooth-level predictions aligns with emerging trends in personalized dental care, as seen in [42], where AI models are tailored for patient-specific interventions. Previous studies have implemented models such as U-Net [43], SegNet [44], BiseNet [45], and Dense-ASPP [46] for dental image segmentation, each of which involves many trainable parameters. The U-Net-based approach focuses on a hybrid loss function weighted on tooth edges rather than architectural modifications. However, their method relied on hyperparameters that may not be optimal and were validated on a limited number of edge-optimized images. Compared to our approach, our model employs optimized hyperparameters, or a more efficient way to enhance prediction accuracy while maintaining computational efficiency. Recent research has shown that BERT-based classification of pediatric dental diseases achieved an accuracy of 77%, while a 1D-CNN reached 84%, outperforming other pretrained CNNs [47]. In contrast, our CNN model leverages direct radiographic image features rather than text-based transformations, enabling more precise tooth localization and treatment prediction. Recent advancements in AI-driven pediatric dental analysis have focused on automating primary teeth segmentation from CBCT scans, achieving expert-level accuracy (98%) and significantly reducing the processing time compared to manual methods [48]. Recent studies have demonstrated the effectiveness of deep learning models, such as YOLOv8, in improving diagnostic accuracy for interproximal caries detection, achieving a high precision of 96.03% for enamel caries and reducing false negatives [49].