Using Artificial Intelligence Methods for Dental Image Analysis: State-of-the-Art Reviews
A large number of studies that use artificial intelligence (AI) methodologies to analyze medical imaging and support computer-aided diagnosis have been conducted in the biomedical engineering domain. Owing to the advances in dental diagnostic X-ray systems such as panoramic radiographs,
periapical radiographs, and dental computed tomography (CT), especially, dual-energy cone beam CT (CBCT), dental image analysis now presents more opportunities to discover new results and findings. Recent researches on dental image analysis have been increasingly incorporating analytics that
utilize AI methodologies that can be divided into conventional machine learning and deep learning approaches. This review first covers the theory on dual-energy CBCT and its applications in dentistry. Then, analytical methods for dental image analysis using conventional machine learning and
deep learning methods are described. We conclude by discussing the issues and suggesting directions for research in future.
Keywords: ARTIFICIAL INTELLIGENCE; DEEP LEARNING; DENTAL COMPUTED TOMOGRAPHY; DUAL-ENERGY CONE BEAM COMPUTED TOMOGRAPHY; MACHINE LEARNING; MEDICAL IMAGE ANALYSIS
Document Type: Review Article
Publication date: 01 November 2020
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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