人工智能应用在口腔颌面放射学中的使用和性能：系统评价。
The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.
机构信息
Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan.
出版信息
Dentomaxillofac Radiol. 2020 Jan;49(1):20190107. doi: 10.1259/dmfr.20190107. Epub 2019 Aug 14.
OBJECTIVES
To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).
METHODS
Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included.
RESULTS
The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms.
CONCLUSION
The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.
目的
探讨人工智能（AI）在口腔颌面放射学（DMFR）中的临床应用现状和诊断性能。
方法
通过检索五个电子数据库和 DMFR 领域的四个选定核心期刊，寻找与 DMFR 相关的应用程序来开发或实施 AI 模型的研究。根据 QUADAS-2 改编了定制的评估标准，用于分析纳入研究的质量。
结果
最初的电子搜索产生了 1862 个标题，最终有 50 项研究入选。大多数研究都集中在 AI 应用于自动定位头影测量标志、诊断骨质疏松症、分类/分割颌面囊肿和/或肿瘤以及识别牙周炎/根尖周疾病上。不同算法的 AI 模型的性能存在差异。
结论
所纳入研究中提出的 AI 模型在 DMFR 中具有广泛的临床应用。然而，在将这些模型转化为临床实践之前，仍有必要进一步验证 AI 模型的可靠性和适用性。
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