Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada.
Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada.
J Esthet Restor Dent. 2023 Sep;35(6):842-859. doi: 10.1111/jerd.13079. Epub 2023 Jul 31.
The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.
An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.
Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.
AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
人工智能(AI)在修复牙科中的应用正在增加;然而,对于牙科专业人员来说,AI 的性能尚不清楚。本叙述性综述的目的是评估 AI 在修复牙科的各个方面的应用、功能和准确性,包括龋齿检测、牙体预备边缘检测、牙体修复设计、金属结构铸造、牙修复/种植体检测、可摘局部义齿设计和牙齿颜色确定。
在 Medline/PubMed、Embase、Web of Science、Cochrane、Scopus 和 Google Scholar 数据库中进行了电子检索。使用人工智能、机器学习、深度学习、人工神经网络、卷积神经网络、聚类、软计算、自动规划、计算学习、计算机视觉和自动推理等关键词,选择了 2000 年 1 月 1 日至 2022 年 3 月 1 日与上述方面相关的英文文章。还进行了手动搜索。因此,纳入了 157 篇文章进行综述和讨论。
根据目前的文献,与传统方法相比,AI 模型在准确性方面在上述方面表现出了有前途的性能;然而,由于这些模型仍在开发中,需要更多的研究来验证它们的准确性,并将其应用于常规临床实践。
具有特定功能的 AI 在修复牙科的各个方面显示出了成功的应用,并且具有可接受的准确性。了解这些功能可能会为 AI 在修复牙科中的应用带来具有最佳准确性的新应用。