## 3 Results
In Figure 4, the confusion matrix visualizes the performance of the model in predicting the need for a space maintainer using 80 flattened images. It provides a detailed breakdown of the true positives, true negatives, false positives, and false negatives. Figure 2: Training accuracy and loss curves for the space maintainer prediction model. The matrix contains four quadrants:  
* The top-left quadrant shows 38 true negatives, where the model correctly predicted that no space maintainer was needed. * The bottom-right quadrant indicates 37 true positives, representing cases where the model accurately identified the need for a space maintainer. * The top-right quadrant reflects two false positives, where the model incorrectly predicted the need for a space maintainer when it was not required. Figure 4: Confusion matrix for space maintainer prediction model. Figure 3: ROC curve illustrating the performance of each CNN model across different classes. * The bottom-left quadrant has three false negatives, indicating that the model missed three cases that required a space maintainer. This performance indicates that the model exhibits a high degree of accuracy and sensitivity, as it successfully identified all true cases requiring intervention (sensitivity) and minimized the occurrence of false positives. The metrics and classification report provides a comprehensive evaluation of the model's performance in predicting the need for a space maintainer. The overall accuracy of the model is 94%, which demonstrates its strong ability to correctly classify both classes (need and no need for a space maintainer). This high accuracy reflects the model's robustness and reliability when applied to dental X-ray data. Table 2 shows the classification report that provides key performance metrics for the model's ability to distinguish between the two classes: cases where a space maintainer is needed (Class 1) and cases where it is not needed (Class 0). For Class 0 (Not Needed), the precision is 0.93, indicating that 93% of the instances predicted as Class 0 were correct. The recall for this class is 0.95, meaning the model successfully identified 95% of actual Class 0 cases. The F1-score, which balances precision and recall, is 0.94, confirming strong performance in detecting cases where a space maintainer is unnecessary. For Class 1 (Needed), the precision is 0.95, meaning that 95% of the instances predicted as Class 1 were correctly classified. The recall is 0.93, showing that the model correctly identified 93% of actual Class 1 cases. The F1-score for this class is also 0.94, indicating an effective balance between precision and recall. The macro average precision, recall, and F1-score are all 0.94, representing the arithmetic mean of these metrics across both classes. Similarly, the weighted average precision, recall, and F1-score are also 0.94, reflecting the overall performance while accounting for class support (number of instances per class). These high values indicate that the model performs consistently well across both classes. The additional metrics, including the ROC AUC score of 0.94 and the MCC of 0.875, underscore the model's high discriminative power and reliability. The ROC AUC score reflects the model's capability to distinguish between the two classes, while the MCC provides a balanced measure of performance, particularly useful for datasets with slight class imbalances. Figure 5 demonstrates the model's predictions for 86 X-ray images, where each image was previously annotated by a dentist to identify the teeth requiring space maintainers. The tooth number on the \(x\)-axis corresponds to the Universal Numbering System, which is commonly used in dentistry to label teeth in a standardized manner. Each point represents a prediction made by the CNN-based AI model, correlating the tooth number with the model's probability of requiring a space maintainer.