Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.

"Declarations Competing interestsThe authors declare that they have no competing financial or personal interests. Ethical ApprovalThis article does not contain any studies with human participants or animals performed by any of the authors. Competing interests The authors declare that they have no competing financial or personal interests."

"Funding Open access funding provided by Politecnico di Milano within the CRUI-CARE Agreement. Dr. Marco Serafin received a scholarship (DOT18AAZ4T/4) through “Programma Operativo Nazionale Ricerca e Innovazione 2014–2020” (CCI 2014IT16M2OP005) attending a PhD course in Translational Medicine of the University of Milan; the present research is funded by project “Artificial intelligence available to the development of an augmented reality software for an automated cephalometric analysis of ultra-reduced CBCT FOV” related to the scholarship DOT18AAZ4T/4."

"The present systematic review was registered to the PROSPERO database (registration number CRD42022315312). The reporting of this study is in accordance with PRISMA statement [] and followed the guidelines in the Cochrane Handbook for Systematic Reviews of Interventions []."