Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.

Journal: Dento maxillo facial radiology
Published Date: Jan 11, 2024

Abstract

OBJECTIVES: Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults.

Authors

  • Ozlem B Dogan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hacettepe University, Sihhiye, Ankara 06230, Turkey.
  • Hatice Boyacioglu
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hacettepe University, Sihhiye, Ankara 06230, Turkey.
  • Dincer Goksuluk
    Department of Biostatistics, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Turkey.

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