Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
J Dent. 2024 Jul;146:105056. doi: 10.1016/j.jdent.2024.105056. Epub 2024 May 8.
The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification.
A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "learning").
A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS.
The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool.
AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis.
Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.
从手动到自动头影测量标志点识别的转变尚未在正畸诊断的临床应用中达成共识。本伞式综述旨在评估人工智能（AI）在自动 2D 和 3D 头影测量标志点识别中的性能。
通过布尔运算符组合自由文本词和 MeSH 关键词：Automa* AND cephalo* AND（“人工智能”或“机器学习”或“深度学习”或“学习”）。
在 PubMed、Scopus、Web of Science、Cochrane 图书馆和 LILACS 上进行了没有时间范围设置的搜索策略。
该研究方案遵循 PRISMA 指南，根据文章的目的制定了 PICO 问题。数据库搜索导致选择了 15 篇全文评估合格的文章。最后，11 篇系统评价符合纳入标准，并根据系统评价偏倚风险（ROBIS）工具进行分析。
AI 无法以相同的准确性识别各种头影测量标志点。由于大多数纳入研究的结论都是基于 AI 自动标志位置与人工操作员分配的位置之间 2 毫米的错误截断差异，因此未来的研究应集中于改进最强大的架构，以提高 AI 驱动的自动头影分析的临床相关性。
尽管性能逐步提高，但 AI 已经超过了大多数头影测量标志点的推荐误差幅度。此外，与 2D X 射线相比，3D CBCT 上的 AI 自动标志定位似乎不太准确。迄今为止，AI 驱动的头影测量标志点定位仍需要经验丰富的正畸医生的最终监督。
