Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.

Journal: The Journal of forensic odonto-stomatology
PMID: 38742569

Abstract

BACKGROUND: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, supervised machine learning modelling -namely support vector regression (SVR) with linear and polynomial kernel, and regression tree - was tested and compared with the multiple linear regression model.

Authors

  • R Merdietio Boedi
    Department of Dentistry, Faculty of Medicine, Universitas Diponegoro, Semarang, Indonesia.
  • S Shepherd
    Department of Oral Surgery, School of Dentistry, University of Dundee, Dundee, United Kingdom.
  • F Oscandar
    Department of Oral and Maxillofacial Radiology - Forensic Odontology, Faculty of Dentistry, Universitas Padjadjaran, Bandung, Indonesia.
  • A J Franco
    Division of Forensic Dentistry, Faculdade São Leopoldo Mandic, Campinas, Brazil.
  • S Manica
    Department of Forensic Odontology, University of Dundee, Nethergate, Dundee DD1 4HN, UK.

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