Forensic dental age estimation with deep learning: a modified xception model for panoramic X-Ray images

Cansu Görürgöz · Hatice Cansu Kış · Emin Murat Canger · Bengi Öztaş

12 citations0/0 files storedForensic Science Medicine and Pathology

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Forensic Anthropology and Bioarchaeology Studies Dental Radiography and Imaging Face recognition and analysis

Abstract

PURPOSE: This study aimed to develop an improved method for forensic age estimation using deep learning models applied to orthopantomography (OPG) images, focusing on distinguishing individuals under 12 years old from those aged 12 and above. METHODS: A dataset of 1941 pediatric patients aged between five and 15 years was collected from two radiology departments. The primary research question addressed the identification of the most effective deep learning model for this task. Various deep learning models including Xception, ResNet, ShuffleNet, InceptionV3, DarkNet, NasNet, DenseNet, EfficientNet, MobileNet, ResNet18, GoogleNet, SqueezeNet, and AlexNet were evaluated using traditional metrics like Classification Accuracy (CA), Sensitivity (SE), Specificity (SP), Kappa (K), Area Under the Curve (AUC), alongside a novel Polygon Area Metric (PAM) designed to handle imbalanced datasets common in forensic applications. RESULTS: "Forensic Xception" model derived from Xception outperformed others, achieving a PAM score of 0.8828. This model demonstrated superior performance in accurately classifying individuals' age groups, with high CA, SE, SP, K, AUC, and F1 Score. Notably, the introduction of the PAM metric provided a comprehensive evaluation of classifier performance. CONCLUSION: This study represents a significant advancement in forensic age estimation from OPG images, emphasizing the potential of deep learning models, particularly the "Forensic Xception" model, in accurately classifying individuals based on age, especially in legal contexts. This research suggests a promising avenue for further advancements in forensic dental age estimation, with future studies encouraged to explore additional datasets, refine models, and address ethical and legal considerations.

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References

2009The UN Convention on the Rights of the Child 2021Automated estimation of chronological age from panoramic dental X-ray images using deep learning 2021Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images 2021Comparison of different machine learning approaches to predict dental age using Demirjian’s staging approach 2022Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters 2015The applicability of Willems' method for age estimation in southern Turkish children: A preliminary study 2023Dental Age Estimation Using Deep Learning: A Comparative Survey 2021Accuracy of the London atlas, Willems, and Nolla methods for dental age estimation: a cross-sectional study on Eastern Turkish children

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