Mirror, Mirror on the Wall: Automating Dental Smile Analysis with AI in Smart Mirrors

This paper presents a smart diagnostic framework for dental smile analysis. To accurately and efficiently identify esthetic issues from a single image of a smile, a convolutional neural network (CNN) was trained. To overcome the limitations of scarce data, a diffusion model was employed to generate dental smile images in addition to manually curated data. The CNN was trained and evaluated on three datasets: all real images, all generated images, and a hybrid dataset comprising of equal proportions of real-to-generated images. All three models demonstrate accuracy significantly above the baseline in detecting excessive gingival display, unlocking a novel diagnostic method in smile analysis. Notably, the hybrid model achieved the highest accuracy of 81.61% (p - value < 0.01), highlighting the effectiveness of generative data augmentation for machine learning. The proposed solution could be part of a standalone home-deployed smart mirror or connected to a network of innovative Internet-of-Mirrors to facilitate patient-dentist communication.