## 4 Discussion
Recent research has demonstrated the effectiveness of CNN models in diagnosing dental diseases by classifying radiographic images into different categories, such as fillings, cavities, and implants [50]. While such segmentation models enhance treatment planning efficiency, they do not predict specific clinical interventions, such as the necessity of a space maintainer. Our CNN model goes beyond segmentation by not only identifying teeth but also determining the teeth number, offering a more comprehensive AI-assisted decision-making tool for pediatric dentistry. The development of a no-code AI model for detecting primary proximal surfaces from bitewing radiographs demonstrates the increasing potential of AI in pediatric dental diagnostics, achieving high accuracy and precision with a limited dataset [51]. While this model focuses on caries detection, our CNN model targets a different aspect of pediatric dentistry by predicting the need for space maintainers and localizing specific teeth for intervention. Both models highlight the value of AI in enhancing diagnostic and treatment planning, but our approach integrates both classification and localization for more comprehensive clinical decision support. Despite the promising performance of the model, several limitations must be considered. Firstly, the dataset size was relatively small, with only 400 images for binary classification and 86 for tooth-level prediction. While the model performed well within this context, a larger, more diverse dataset would be crucial to improve its generalizability and robustness, ensuring that it can perform effectively across a broader range of cases. Additionally, the model was trained and tested on a single dataset, which may limit its applicability to other populations or imaging modalities. Variations in dental X-ray characteristics--such as differences in equipment, settings, and patient demographics--could influence the model's performance when applied to different clinical environments. Moreover, while the dataset was balanced for the binary classification task, there were slight imbalances in the tooth-specific predictions, which could impact the model's accuracy for underrepresented categories. Lastly, error analysis revealed higher prediction errors for tooth numbers 75 and 84, suggesting the need for further investigation to determine whether these errors are due to model limitations, dataset bias, or the inherent challenges of interpreting certain dental structures. To better understand the misclassifications, we analyzed the prediction probabilities across different tooth numbers (Figure 5). The results indicate that errors are primarily concentrated around tooth numbers 65 and 75, suggesting potential feature overlap between these teeth. Additionally, our confusion matrix (Figure 4) reveals that false positives (n = 2) and false negatives (n = 3) occur in cases with reduced contrast or anatomical similarity to adjacent teeth. Furthermore, inconsistencies in the radiographic quality, such as variations in brightness and contrast, may have contributed to the observed errors. To mitigate these issues, future improvements will focus on data augmentation strategies to enhance feature diversity, the optimization of classification thresholds, and potential architectural modifications to strengthen feature extraction in ambiguous cases. Collaborations with multiple institutions or incorporating public datasets could improve the model's generalizability. Extending the model to handle multi-class predictions would broaden its clinical applicability by enabling it to classify additional dental conditions beyond the need for SMs. Leveraging transfer learning techniques with pretrained models on larger datasets, such as ImageNet, could further enhance performance, particularly when dealing with limited datasets. Incorporating explainability methods like Grad-CAM could provide valuable insights into the model's decision-making process, increasing trust and usability among clinicians.