Learning Beyond Human Expertise with Generative Models for Dental Restorations

TL;DR

This paper introduces a GAN-based deep learning model that automatically designs dental crowns by learning from human expert designs and natural tooth profiles, surpassing human standards in morphology and functionality.

Contribution

The paper presents a novel fully automatic generative approach for dental crown design that incorporates spatial and statistical constraints, improving over traditional manual methods.

Findings

Automatic designs exceed human standards in morphology.

The model effectively learns from both expert designs and natural tooth profiles.

The approach is being tested for production use.

Abstract

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.