Literature DB >> 32575560

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

Kuofeng Hung1, Andy Wai Kan Yeung1, Ray Tanaka1, Michael M Bornstein1,2.   

Abstract

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.

Entities:  

Keywords:  AI; ML; artificial intelligence; cone beam computed tomography (CBCT); facial scanning; intraoral scanning; machine learning

Year:  2020        PMID: 32575560     DOI: 10.3390/ijerph17124424

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  6 in total

Review 1.  The Modern and Digital Transformation of Oral Health Care: A Mini Review.

Authors:  Muhammad Syafiq Alauddin; Ahmad Syukran Baharuddin; Mohd Ifwat Mohd Ghazali
Journal:  Healthcare (Basel)       Date:  2021-01-25

Review 2.  The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.

Authors:  Julien Issa; Raphael Olszewski; Marta Dyszkiewicz-Konwińska
Journal:  Int J Environ Res Public Health       Date:  2022-01-04       Impact factor: 3.390

3.  Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study.

Authors:  Francesca De Angelis; Nicola Pranno; Alessio Franchina; Stefano Di Carlo; Edoardo Brauner; Agnese Ferri; Gerardo Pellegrino; Emma Grecchi; Funda Goker; Luigi Vito Stefanelli
Journal:  Int J Environ Res Public Health       Date:  2022-02-02       Impact factor: 3.390

4.  Radiolucent Lesions of the Jaws: An Attempted Demonstration of the Use of Co-Word Analysis to List Main Similar Pathologies.

Authors:  Andy Wai Kan Yeung
Journal:  Int J Environ Res Public Health       Date:  2022-02-09       Impact factor: 3.390

5.  The Diagnostic Relevance and Interfaces Covered by Mach Band Effect in Dentistry: An Analysis of the Literature.

Authors:  Andy Wai Kan Yeung
Journal:  Healthcare (Basel)       Date:  2022-03-28

6.  Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.

Authors:  Nermin Morgan; Adriaan Van Gerven; Andreas Smolders; Karla de Faria Vasconcelos; Holger Willems; Reinhilde Jacobs
Journal:  Sci Rep       Date:  2022-05-07       Impact factor: 4.996

  6 in total

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