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 language | English |
---|---|
Article number | 17279 |
Number of pages | 12 |
Journal | Scientific Reports |
Volume | 12 |
DOIs | |
Publication status | Published - 14 Oct 2022 |
Keywords
- CNN
- Forensic dentistry
- Human identification
- Machine learning
- Radiology
- Dental anthropology
- Dental radiology
ASJC Scopus subject areas
- General