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

Journal: La Radiologia medica
Published Date: Apr 24, 2023

Abstract

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.

Authors

  • Marco Serafin
    Department of Biomedical Sciences for Health, University of Milan, Via Mangiagalli 31, 20133, Milan, Italy.
  • Benedetta Baldini
    Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Via Ponzio 34/5, 20133, Milan, Italy. benedetta.baldini@polimi.it.
  • Federico Cabitza
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy.
  • Gianpaolo Carrafiello
    Radiology Department, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy.
  • Giuseppe Baselli
  • Massimo Del Fabbro
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Via della Commenda 10, 20122, Milan, Italy.
  • Chiarella Sforza
    Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan.
  • Alberto Caprioglio
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Via della Commenda 10, 20122, Milan, Italy.
  • Gianluca M Tartaglia
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Via della Commenda 10, 20122, Milan, Italy.

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