Using Artificial Intelligence Methods for Dental Image Analysis: State-of-the-Art Reviews

被引:3
作者
Ahn, Junho [1 ]
Ho, Thi Kieu Khanh [2 ]
Kang, Jaeyong [1 ]
Gwak, Jeonghwan [1 ]
机构
[1] Korea Natl Univ Transportat, Dept Software, Chungju 27469, South Korea
[2] Gwangju Inst Sci & Technol, Sch Elect Engn & Comp Sci, Gwangju 61005, South Korea
基金
新加坡国家研究基金会;
关键词
Dental Computed Tomography; Dual-Energy Cone Beam Computed Tomography; Artificial Intelligence; Machine Learning; Deep Learning; Medical Image Analysis; BEAM COMPUTED-TOMOGRAPHY; FALSE-POSITIVE REDUCTION; AIDED DETECTION; AUTOMATIC SEGMENTATION; NODULE DETECTION; LUNG NODULES; CT; CLASSIFICATION; PERFORMANCE; DIAGNOSIS;
D O I
10.1166/jmihi.2020.3254
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:2532 / 2542
页数:11
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