Space maintainers (SMs) are essential for preserving dental arch integrity after premature tooth loss. This study aimed to develop a deep learning model to predict the necessity of SMs and identify specific teeth requiring intervention. A dataset of 400 dental X-rays was preprocessed to standardize image dimensions and convert them into numerical representations for machine learning. The dataset was divided into training (80%) and testing (20%) subsets. A Convolutional Neural Network (CNN) was designed with multiple convolutional and pooling layers, followed by fully connected layers for binary classification. The model was trained using 30 epochs and evaluated with accuracy, precision, recall, F1-score, ROC AUC, and MCC. The CNN achieved 94% accuracy, with a precision of 0.93 for Class 0 (no SM needed) and 0.95 for Class 1 (SM needed). The ROC AUC was 0.94, and the MCC was 0.875, indicating strong reliability. When tested on 86 X-ray images, the model successfully identified specific teeth (showing teeth number) requiring SMs, with minimal errors. These results suggest that the proposed AI model provides high-performance predictions for SM necessity, offering a valuable decision-support tool for pediatric dentistry.

Background/Objectives: The first permanent molar (M1) is critical for occlusal development but is highly susceptible to caries and molar–incisor hypomineralization (MIH). When M1 prognosis is poor, extraction may be necessary, requiring accurate evaluation for post-extraction space management. This study aims to develop and validate an automated deep learning framework using YOLOv8n with oriented bounding boxes (OBB) to predict the likelihood of spontaneous space closure following M1 extractions, thereby reducing diagnostic subjectivity and inter-observer variability. Methods: A dataset of 200 pediatric panoramic radiographs was segmented into quadrants and annotated for second permanent molars (M2s) and third molars (M3s). The YOLOv8n-OBB architecture was trained on 640 × 640 pixel images over 100 epochs. The framework integrated M3 presence, M2 Demirjian developmental maturity (proxied by bounding box height), and M2 angulation (via rotation vectors) to map inputs onto an evidence-based clinical decision matrix for prognostic stratification. Results: The model achieved exceptional detection and localization performance with an overall mean average precision (mAP@0.5) of 0.983. Class-specific validation showed high accuracy for M2 (F1-score = 0.978) and M3 (F1-score = 0.904). Quantitative cross-referencing confirmed a seamless mapping of spatial coordinates onto clinical success classes without error propagation. Conclusions: These findings substantiate the YOLOv8n-OBB model as a robust and interpretable decision-support tool. By standardizing prognostic assessments and optimizing treatment planning workflows, the framework serves as an effective aid in pediatric dentistry for managing M1 extractions.