Diagnostic Performance of convolutional neural networks for dental sexual dimorphism

Ademir Franco do Rosario Junior (Lead / Corresponding author), Lucas Porto, Dennis Heng, Jared Murray, Anna Lygate, Raquel Franco, Juliano Bueno, Marilia Sobania, Márcio M. Costa, Luiz R. Paranhos (Lead / Corresponding author), Scheila Manica, Andre Abade

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Abstract

Convolutional neural networks (CNN) led to important solutions in the field of Computer Vision. More recently, forensic sciences benefited from the resources of artificial intelligence, especially in procedures that normally require operator-dependent steps. Forensic tools for sexual dimorphism based on morphological dental traits are available but have limited performance. This study aimed to test the application of a machine learning setup to distinguish females and males using dentomaxillofacial features from a radiographic dataset. The sample consisted of panoramic radiographs (n = 4,003) of individuals in the age interval of 6 and 22.9 years. Image annotation was performed with V7 software (V7labs, London, UK). From Scratch (FS) and Transfer Learning (TL) CNN architectures were compared, and diagnostic accuracy tests were used. TL (82%) performed better than FS (71%). The correct classifications of females and males aged ≥15 years were 87% and 84%, respectively. For females and males
Original languageEnglish
Article number17279
Number of pages12
JournalScientific Reports
Volume12
DOIs
Publication statusPublished - 14 Oct 2022

Keywords

  • CNN
  • Forensic dentistry
  • Human identification
  • Machine learning
  • Radiology
  • Dental anthropology
  • Dental radiology

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