Learning Beyond Human Expertise with Generative Models for Dental Restorations

By Jyh-Jing Hwang, Sergei Azernikov, Alexei A. Efros, Stella X. Yu. 2018

42 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xi Li
3. Zhenan Sun
4. Jieping Ye
5. Jie Gui
6. Luc Van Gool
7. Stefano Ermon
8. Shuicheng Yan

In Signal Processing

1. Dacheng Tao
2. Jie Gui
3. Jieping Ye
4. Yonggang Wen
5. Bourahla Omar
6. Zhenan Sun
7. Aditya Grover
8. Rui Shu

Citations in the corpus

• Recent Progress on Generative Adversarial Networks (GANs): A Survey (2019, background): “Furthermore, for private product customization, Hwang et al. [68] utilized GANs to manufacture medical products.”
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, methodology): “Medical field: GANs have been widely utilized in medical field such as generating and designing DNA [404], [405], drug discovery [406], generating multi-label discrete patient records [407], medical image processing [408]–[415], dental restorations [416], and doctor recommendation [417].”
• Fair Generative Modeling via Weak Supervision (2019, methodology): “Examples of such production level systems include Wavenet for text-to-speech synthesis [1], generative adversarial networks (GAN) for dental restoration [2], and a large number of creative applications such Coconet used for designing the “first AI-powered Google Doodle” [3].”
• Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey (2019, background): “…reconstruction (Xu et al. 2017)], anomaly detection (Schlegl et al. 2017), 3D object generation (Wu et al. 2016a) [for instance dental restoration (Hwang et al. 2018)], music generation (Yang et al. 2017), scientific simulations acceleration [for example in astrophysics (Mustafa et al. 2017) or…”
• A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, background): “GANbased teeth restoration system [86] proposed to restore the missing tooth crown.”