Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews

Abstract

ObjectivesThe 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. DataA 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”). SourcesA search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS. Study selectionThe 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. ConclusionsAI 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. Clinical significanceDespite 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.