Do machine learning methods solve the main pitfall of linear regression in dental age estimation?

Journal: Forensic science international
PMID: 39733693

Abstract

INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains deteriorate. Recently, Machine Learning algorithms have been used in age estimation, demonstrating high levels of accuracy. However, their precision with respect to the trend of age estimation error, typical in some traditional methods like linear regression, has not been thoroughly investigated.

Authors

  • Andrea Faragalli
    Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona 60126, Italy. Electronic address: a.faragalli@staff.univpm.it.
  • Luigi Ferrante
    Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona 60126, Italy.
  • Nikolaos Angelakopoulos
    Department of Orthodontics and Dentofacial Orthopedics, University of Bern, Freiburgstrasse 7, Bern 3010, Switzerland.
  • Roberto Cameriere
    AgEstimation Project, Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.
  • Edlira Skrami
    Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona 60126, Italy.

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