Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.

Journal: BMC oral health
PMID:

Abstract

BACKGROUND: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL).

Authors

  • Domenico Albano
    IRCCS Istituto Ortopedico Galeazzi, Milano, Italy; Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy.
  • Vanessa Galiano
    School of Dentistry, University of Milano, Milan, Italy.
  • Mariachiara Basile
    Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy.
  • Filippo Di Luca
    Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy.
  • Salvatore Gitto
    Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy. Electronic address: sal.gitto@gmail.com.
  • Carmelo Messina
    Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy; IRCCS Istituto Ortopedico Galeazzi, Milano, Italy.
  • Maria Grazia Cagetti
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
  • Massimo Del Fabbro
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Via della Commenda 10, 20122, Milan, Italy.
  • Gianluca Martino Tartaglia
    Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
  • Luca Maria Sconfienza
    Unit of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.