Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

• Main Authors: Kuofeng Hung, Andy Wai Kan Yeung, Ray Tanaka, Michael M. Bornstein
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: artificial intelligence; AI; machine learning; ML; cone beam computed tomography (CBCT); intraoral scanning
• Online Access: https://www.mdpi.com/1660-4601/17/12/4424

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.