Learning Beyond Human Expertise with Generative Models for Dental Restorations
This addresses the time-consuming and labor-intensive process of custom dental crown design in the dental industry, offering a fully automatic solution that improves efficiency and quality.
The paper tackles the problem of automating dental crown design, which traditionally requires extensive human expertise, by developing a generative model that learns from human designs and natural spatial profiles between teeth. The result is an automatic approach that exceeds human technicians' standards for morphology and functionality, with the algorithm being tested for production use.
Computer vision has advanced significantly that many discriminative approaches such as object recognition are now widely used in real applications. We present another exciting development that utilizes generative models for the mass customization of medical products such as dental crowns. In the dental industry, it takes a technician years of training to design synthetic crowns that restore the function and integrity of missing teeth. Each crown must be customized to individual patients, and it requires human expertise in a time-consuming and labor-intensive process, even with computer-assisted design software. We develop a fully automatic approach that learns not only from human designs of dental crowns, but also from natural spatial profiles between opposing teeth. The latter is hard to account for by technicians but important for proper biting and chewing functions. Built upon a Generative Adversar-ial Network architecture (GAN), our deep learning model predicts the customized crown-filled depth scan from the crown-missing depth scan and opposing depth scan. We propose to incorporate additional space constraints and statistical compatibility into learning. Our automatic designs exceed human technicians' standards for good morphology and functionality, and our algorithm is being tested for production use.