AI-Powered Prediction of Dental Space Maintainer Needs Using X-Ray Imaging: A CNN-Based Approach for Pediatric Dentistry

Abstract

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.