Synthesizing Mandibular Third Molars with Denoising Diffusion Probabilistic Models and Generative Adversarial Networks
Abstract
In oral surgery, the classification of the proximity of the mandibular third molar to the mandibular canal, typically performed on panoramic radiographs, is essential for surgical planning. While artificial intelligence (AI) tools have been explored for this task, their performance is limited due to data scarcity and class imbalance. In this work, we study the potential of synthetic data generation for this task using Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), both unconditional and conditioned to the tooth-canal relationship. We used public datasets to create and label a training dataset of 5416 images. The results show the lowest Fréchet inception distance (FID) / second-highest Inception Score (IS) for the unconditional GAN (32.48 / 2.14). The unconditional DDPM showed an FID of 34.28 and IS of 1.95. Conditional models showed similar IS but a worse overall FID of 68.19 and 219.11 for DDPM and GAN, respectively. In a paired observer study between the two unconditional models, clinical observers found the DDPM image to be more realistic in 69.6% of cases. Future work should investigate downstream effects of GANs and DDPMs used in data augmentation for the training of an AI classifier.
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