Information fusion for infant age estimation from deciduous teeth using machine learning.

Journal: American journal of biological anthropology
PMID: 38400830

Abstract

OBJECTIVES: Over the past few years, several methods have been proposed to improve the accuracy of age estimation in infants with a focus on dental development as a reliable marker. However, traditional approaches have limitations in efficiently combining information from different teeth and features. In order to address these challenges, this article presents a study on age estimation in infants with Machine Learning (ML) techniques, using deciduous teeth.

Authors

  • Práxedes Martínez-Moreno
    Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain.
  • Andrea Valsecchi
    European Center for Soft Computing, C/Gonzalo Gutiérrez Quirós, s/n - 3(a) planta, 33600 Mieres, Spain. Electronic address: andrea.valsecchi@softcomputing.es.
  • Sergio Damas
    European Center for Soft Computing, C/Gonzalo Gutiérrez Quirós, s/n - 3(a) planta, 33600 Mieres, Spain. Electronic address: sergio.damas@softcomputing.es.
  • Javier Irurita
    Department of Legal Medicine, Toxicology and Physical Anthropology, University of Granada, 8012, Avenida de la Investigación 11, Granada, Spain. javieri@ugr.es.
  • Pablo Mesejo
    Department of Information Engineering, University of Parma, Parma 43124, Italy , ISIT-UMR 6284 CNRS, University of Auvergne, Clermont-Ferrand 63000, France.

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