A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

Scientific paper - Original scientific paper

• Author: Denis Milošević
• Author: Marin Vodanović
• Author: Ivan Galić
• Author: Marko Subašić
• Author's institution: (University Hospital of Split)
• Author's institution: (Zagreb University Hispital Center)
• Author's institution: University of Zagreb; Faculty of Electrical Engineering and Computing
• Author's institution: University of Zagreb; School of Dental Medicine
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Dental Medicine

Abstract (english)

Determining the demographic characteristics of a person post-mortem is a fundamental task for forensic experts, and the dental system is a crucial source of those information. Those characteristics, namely age and sex, can reliably be determined. The mandible and individual teeth survive even the harshest conditions, making them a prime target for forensic analysis. Current methods in forensic odontology rely on time- consuming manual measurements and reference tables, many of which rely on the correct determination of the tooth type. This study thoroughly explores the applicability of deep learning for sex assessment, age estimation, and tooth type determination from x-ray images of individual teeth. A series of models that use state-of-the-art feature extraction architectures and attention have been trained and evaluated. Their hyperparameters have been explored and optimized using a combination of grid and random search, totaling over a thousand experiments and 14076 hours of GPU compute time. Our dataset contains 86495 individual tooth x-ray image samples, with a subset of 7630 images having additional information about tooth alterations. The best-performing models are fine-tuned, the impact of tooth alterations is analyzed, and model performance is compared to current methods in forensic odontology literature. We achieve an accuracy of 76.41% for sex assessment, a median absolute error of 4.94 years for age estimation, and an accuracy of 87.24% to 99.15% for tooth type determination. The constructed models are fully automated and fast, their results are reproducible, and the performance is equal to or better than current state-of-the-art methods in forensic odontology.

• Keywords (english): age estimation; sex assessment; tooth type determination; tooth numbering; convolutional neural network; deep learning; forensic odontology; dental x-ray; image processing; medical image analysis
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Journal title: IEEE Access
• Numbering: vol. 10, pp. 70980-71002
• e-ISSN: 2169-3536
• DOI: https://doi.org/10.1109/ACCESS.2022.3187959
• URN:NBN: urn:nbn:hr:127:593840
• Publication: 2022
• Document URL: https://ieeexplore.ieee.org/document/9813689/
• Type of resource: Text
• Access conditions: Open access
• Created on: 2024-03-13 10:14:14