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

OBJECTIVES: The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. METHODS: PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases...

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Detalles Bibliográficos
Autores principales:	Serafin, Marco, Baldini, Benedetta, Cabitza, Federico, Carrafiello, Gianpaolo, Baselli, Giuseppe, Del Fabbro, Massimo, Sforza, Chiarella, Caprioglio, Alberto, Tartaglia, Gianluca M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Milan 2023
Materias:	Computed Tomography
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181977/ https://www.ncbi.nlm.nih.gov/pubmed/37093337 http://dx.doi.org/10.1007/s11547-023-01629-2

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