A machine learning approach for age prediction based on trigeminal landmarks.

Journal: Journal of forensic and legal medicine

PMID: 39213905

Abstract

OBJECTIVE: The aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT).

Authors

Youssef Keyrouz

Vancouver, BC, Canada.
Marianne Saade

Virginia, USA.
Mona Nahas Gholmieh

Department of Pediatric Dentistry and Dental Public Health, Faculty of Dental Medicine- Lebanese University, Beirut, Lebanon.
Antoine Saadé

Department of Orthodontics and Dentofacial Orthopedics, Faculty of Dental Medicine- Lebanese University, Beirut, Lebanon. Electronic address: saadeantoine@hotmail.com.

Keywords

Adolescent Age Determination by Skeleton Algorithms Anatomic Landmarks Child Cone-Beam Computed Tomography Decision Trees Female Humans Machine Learning Male Principal Component Analysis Young Adult

External Resources

View on PubMed Access via DOI PubMed (39213905)

A machine learning approach for age prediction based on trigeminal landmarks.

Abstract

Authors

Keywords

External Resources

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