PDF(Pubmed)
Abstract：
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was \"What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?\" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.
摘要：
背景和目的：多种牙科植入物系统的可用性使治疗牙医难以在无法接近或丢失先前记录的情况下识别和分类植入物。据报道，人工智能（AI）在医学图像分类中具有很高的成功率，并在该领域得到了有效的应用。研究报告说，当AI与训练有素的牙科专业人员一起使用时，植入物分类和识别准确性得到了提高。本系统综述旨在分析各种研究，讨论AI工具在植入物识别和分类中的准确性。方法：遵循系统评价和荟萃分析（PRISMA）指南的首选报告项目，该研究已在国际前瞻性系统审查登记册（PROSPERO）注册。当前研究的焦点PICO问题是“人工智能工具（干预）在使用X射线图像检测和/或分类牙科植入物类型（参与者/人群）方面的准确性（结果）是什么？”Scopus,MEDLINE-PubMed,和Cochrane进行了系统的搜索，以收集相关的已发表文献。搜索字符串基于公式化的PICO问题。文章搜索是在2024年1月使用布尔运算符和截断进行的。搜索仅限于过去15年（2008年1月至2023年12月）以英文发表的文章。使用质量评估和诊断准确性工具（QUADAS-2）对所有选定文章的质量进行了严格分析。结果：根据预定的选择标准，选择21篇文章进行定性分析。研究特征在自行设计的表格中列出。在评估的21项研究中，14人被发现有偏见的风险，在一个或多个领域具有高风险或不明确的风险。其余七项研究，然而,偏见的风险很低。AI模型在植入物检测和识别中的总体准确性从67％的低到98.5％。大多数纳入的研究报告平均准确率超过90%。结论：本综述中的文章提供了大量证据来验证AI工具在使用二维X射线图像识别和分类牙科植入物系统方面具有很高的准确性。这些结果对于训练有素的牙科专业人员的临床诊断和治疗计划至关重要，以提高患者的治疗结果。
