## 2 Methods
### AI Architecture and Performance Metrics  
The predictive system for determining the need for space maintainers was built using a CNN, designed to process and analyze dental X-ray images. The architecture consisted of multiple convolutional and pooling layers, followed by fully connected layers to perform binary classification. The specific layers and their configurations were as follows:1. Convolutional Layers: \(\odot\) The first convolutional layer consisted of 256 filters with a kernel size of 3 \(\times\) 3 and ReLU activation. This layer extracted low-level spatial features from the input images. \(\odot\) Subsequent layers included 256 filters, also with a kernel size of 3 \(\times\) 3 and ReLU activation. These layers progressively extracted higher-level features essential for classification. 2. Pooling Layers: \(\odot\) Max-pooling layers with a pool size of 2 \(\times\) 2 were interspersed between convolutional layers to reduce the spatial dimensions of the feature maps, thereby controlling overfitting and improving computational efficiency. 3. Fully Connected Layers: \(\odot\) A dense layer with 256 neurons and ReLU activation was added to integrate the extracted features. \(\odot\) The final dense layer consisted of a single neuron with sigmoid activation, outputting a probability score for binary classification (0: no space maintainer needed, 1: space maintainer needed).  
Figure 1 demonstrates the graphical representation of the CNN architecture, illustrating the layers and connections that enable the model to perform image classification. The model was trained using 30 epochs and a size batch of 64 to balance computational efficiency and convergence. A binary cross-entropy loss function was employed, which is well suited for binary classification tasks, and an optimizer was utilized to minimize the loss, ensuring efficient convergence during training. The model's parameters were fine-tuned to optimize its predictive capabilities and to generalize well to unseen data. The performance of the CNN model was evaluated using a comprehensive set of metrics to capture its predictive capabilities and reliability.  
Table 1 shows performance metrics such as accuracy. Accuracy was used to measure the overall correctness of the model's predictions, while precision evaluated the proportion of true positive predictions among all positive predictions, indicating the model's ability to avoid false positives. Recall, or sensitivity, assessed the proportion of true positive cases detected, highlighting the model's ability to identify cases requiring space maintainers. The F1-score, a harmonic means of precision and recall, provided a balanced assessment of the model's performance. Additionally, the Receiver Operating Characteristic Area  
Figure 1: Graphical representation of the CNN architecture.  
Under Curve (ROC AUC) quantified the model's ability to distinguish between classes, with higher values indicating better discriminatory power. It is computed as the area under the ROC curve, which plots the true positive rate (TPR) against the false positive rate (FPR). The Matthews Correlation Coefficient (MCC) was also calculated to evaluate the correlation between predicted and true labels, accounting for both true and false predictions, which made it particularly useful for imbalanced datasets.  
The comprehensive evaluation of these metrics ensured a robust assessment of the model's performance, emphasizing its clinical relevance. High precision and recall scores were critical in this application to minimize false positives, which could lead to unnecessary interventions, and false negatives, which might result in missed cases requiring intervention. The ROC AUC and MCC further validated the model's robustness, demonstrating its reliability in accurately predicting the need for space maintainers across diverse cases. By achieving high performance across these metrics, the system showed potential to assist dental professionals in making faster and more accurate clinical decisions.