Department of Biomaterials and Experimental Dentistry, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland.
Department of Oral and Maxilofacial Surgery, Cliniques Universitaires Saint Luc, UCLouvain, Av. Hippocrate 10, 1200 Brussels, Belgium.
Int J Environ Res Public Health. 2022 Jan 4;19(1):560. doi: 10.3390/ijerph19010560.
This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well as to present their diagnostic accuracy. Articles related to inferior alveolar nerve/canal localization using methods based on artificial intelligence (semi-automated and fully automated) were collected electronically from five different databases (PubMed, Medline, Web of Science, Cochrane, and Scopus). Two independent reviewers screened the titles and abstracts of the collected data, stored in EndnoteX7, against the inclusion criteria. Afterward, the included articles have been critically appraised to assess the quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Seven studies were included following the deduplication and screening against exclusion criteria of the 990 initially collected articles. In total, 1288 human cone-beam computed tomography (CBCT) scans were investigated for inferior alveolar canal localization using different algorithms and compared to the results obtained from manual tracing executed by experts in the field. The reported values for diagnostic accuracy of the used algorithms were extracted. A wide range of testing measures was implemented in the analyzed studies, while some of the expected indexes were still missing in the results. Future studies should consider the new artificial intelligence guidelines to ensure proper methodology, reporting, results, and validation.
本系统评价旨在确定可用于定位下牙槽管的半自动和全自动算法，并介绍其诊断准确性。通过人工智 能（半自动和全自动）方法，从五个不同的数据库（PubMed、Medline、Web of Science、Cochrane 和 Scopus） 中电子收集与下牙槽神经/管定位相关的文章。两名独立的审查员根据纳入标准筛选收集到的存储在 EndnoteX7 中的标题和摘要。之后，使用诊断准确性研究的质量评估-2（QUADAS-2）工具对纳入的文章进行严格评估，以评估研究的质量。在对最初收集的 990 篇文章进行去重和排除标准筛选后，共纳入了 7 篇研究。总共使用不同的算法对 1288 个人类锥形束 CT（CBCT）扫描进行了下牙槽管定位研究，并将其与领域专家手动追踪的结果进行了比较。提取了所使用算法的诊断准确性报告值。在分析的研究中实施了广泛的测试措施，而结果中仍缺少一些预期的指标。未来的研究应考虑新的人工智能指南，以确保适当的方法、报告、结果和验证。
