Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review

被引:9
作者
Alharbi, Shuaa S. [1 ]
Alhasson, Haifa F. [1 ]
机构
[1] Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah 52571, Saudi Arabia
关键词
artificial intelligent; diagnostic imaging; diagnosis; deep learning; deep neural networks; machine learning; medical image processing; systematic review; BEAM COMPUTED-TOMOGRAPHY; NEURAL-NETWORKS; COMPROMISED TEETH; SEGMENTATION; CLASSIFICATION; RECOGNITION; DIAGNOSIS; ACCURACY; CARIES; MODEL;
D O I
10.3390/diagnostics14212442
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.
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页数:28
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