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 w...

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Veröffentlicht in:	Radiologia medica 2023-05, Vol.128 (5), p.544-555
Hauptverfasser:	Serafin, Marco, Baldini, Benedetta, Cabitza, Federico, Carrafiello, Gianpaolo, Baselli, Giuseppe, Del Fabbro, Massimo, Sforza, Chiarella, Caprioglio, Alberto, Tartaglia, Gianluca M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Anatomic Landmarks Annotations Automation Cephalometry - methods Computed Tomography Deep Learning Diagnostic Radiology Heterogeneity Humans Imaging Imaging, Three-Dimensional - methods Interventional Radiology Machine learning Medical imaging Medicine Medicine & Public Health Meta-analysis Neuroradiology Qualitative analysis Radiology Reproducibility of Results Systematic review Ultrasound
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