Tooth Development Prediction Using a Generative Machine Learning Approach

This study pioneers the use of generative deep learning in pediatric dentistry to predict dental growth using panoramic radiography, going beyond numerical analysis and providing dynamic representations of tooth development. We employed StyleGAN-XL, a state-of-the-art generative adversarial network (GAN), to generate realistic images of dental development stages in children. Our dataset consisted of 8,092 anonymized panoramic radiographs from Osaka University Dental Hospital containing various dentition stages and conditions. By interpolating latent vectors from primary or mixed dentition images with those from permanent dentition, we generated continuous transitioning images that visually represented the progression of dental development. The performance of the StyleGAN-XL model was evaluated using Fr & eacute;chet inception distance scores. Pivotal tuning inversion was used to project real images onto the model's latent space, allowing us to effectively interpolate between current and future dental states. The resulting images showed a smooth transition from primary to permanent dentition, closely resembling the actual stages of dental development. This method represents a significant advancement in dental imaging and predictive analytics, offering a novel approach for clinicians and patients to visualize and understand dental growth. Our findings suggest broader applications for generative models in medical imaging, extending beyond traditional enhancement and modeling tasks. Our study highlights the transformative potential of GANs in medical imaging and provides a foundation for future advancements in predictive dentistry.