Supervised learning involves analyzing labeled input data to uncover patterns, utilizing models such as Bayesian inference, decision trees, linear discriminants, support vector machines, logistic regression, and artificial neural networks [23]. Deep learning, a more advanced subset of ML, employs multiple interconnected layers to extract features and optimize model performance [24]. AI technologies aim to develop systems and robots capable of performing tasks like pattern recognition, decision making, and adaptive problem solving--capabilities traditionally associated with human intelligence [25]. Advances in computational power, combined with innovations in machine learning techniques and neural networks, have accelerated progress in AI [26]. As a subset of AI, ML focuses on training computers to analyze large datasets, identify trends, and apply these insights for predictions or decisions [25]. AI has demonstrated transformative potential across fields such as natural language processing, autonomous vehicles, healthcare, and image recognition. In AD research, it excels at rapidly analyzing complex datasets, identifying patterns imperceptible to humans, and providing highly accurate predictions, thereby advancing the understanding and management of the disease [27, 28]. DL is centered around advanced neural network architectures, including Convolutional Neural Networks (CNNs) [29] and artificial neural networks (ANNs) [30]. CNNs are a specialized type of ANN designed to process and analyze visual data, such as images. Unlike ANNs, CNNs leverage convolutional layers that apply filters (kernels) to extract spatial and hierarchical features like edges, textures, and shapes [31]. AI is utilized in various areas of pediatric dentistry, including dental plaque detection, oral health assessment, supernumerary tooth identification, detection of early childhood caries, fissure sealant categorization, chronological age assessment, deciduous and young permanent tooth detection, ectopic eruption detection, and behavior management. Previous studies have compared the clinical success of traditional and 3D-printed space maintainers [32]. One study has evaluated the effectiveness of AI-based chatbots in providing reliable and high-quality information on space maintainers for pediatric patients and their parents [33]. A recent study developed a CNN model based on YOLOv3 to detect mesiodens in panoramic radiographs and identify specific tooth numbers, achieving high accuracy [34]. Another study has shown the effectiveness of CNNs in dental image analysis, particularly in detecting third molar angles using object detection models [35]. Unlike traditional deep learning models that focus solely on tooth type classification, such as the ZNet model proposed in [36], our CNN model not only predicts the necessity of a space maintainer (SM) but also identifies the specific tooth requiring intervention, providing a more clinically actionable solution for pediatric dentistry. This dual-function capability uniquely enhances clinical decision making by providing precise localization, which is crucial for pediatric dentists. By integrating both classification and localization, our model streamlines the treatment planning process, reducing reliance on time-consuming manual assessments. The CNN model has the potential to significantly reduce errors attributable to human oversight and may serve as a valuable resource for guiding future dental professionals [19]. This innovation bridges the gap between AI-driven dental diagnostics and real-world clinical applications, making it a valuable tool for digital dentistry. This study aims to leverage the capabilities of AI to facilitate the automated assessment of the need for an SM through the detailed analysis of dental radiographic imagery. It is hypothesized that the AI model will accurately predict the need for a space maintainer and identify specific teeth requiring intervention, thus providing a reliable and efficient tool for pediatric dentists in clinical practice.