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Publisher DOI: 10.1007/s00414-020-02465-z
Title: Automated age estimation of young individuals based on 3D knee MRI using deep learning
Language: English
Authors: Auf der Mauer, Markus 
Jopp-van Well, Eilin 
Herrmann, Jochen 
Groth, Michael 
Morlock, Michael 
Maas, Rainer 
Säring, Dennis 
Issue Date: 17-Dec-2020
Publisher: Springer
Source: International Journal of Legal Medicine 135 (2): 649-663 (2021)
Journal or Series Name: International journal of legal medicine 
Abstract (english): 
Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects. However, automated and user-independent approaches are required to perform reliable assessments on large datasets. The aim of this study was to develop a fully automated and computer-based method for age estimation based on 3D knee MRIs using machine learning. The proposed solution is based on three parts: image-preprocessing, bone segmentation, and age estimation. A total of 185 coronal and 404 sagittal MR volumes from Caucasian male subjects in the age range of 13 and 21 years were available. The best result of the fivefold cross-validation was a mean absolute error of 0.67 ± 0.49 years in age regression and an accuracy of 90.9%, a sensitivity of 88.6%, and a specificity of 94.2% in classification (18-year age limit) using a combination of convolutional neural networks and tree-based machine learning algorithms. The potential of deep learning for age estimation is reflected in the results and can be further improved if it is trained on even larger and more diverse datasets.
DOI: 10.15480/882.3302
ISSN: 0937-9827
Institute: Biomechanik M-3 
Document Type: Article
More Funding information: This project is funded by the German Research Foundation (DFG), Project (SA 2530/6-1) and (JO 1198/2-1).
Project: Projekt DEAL 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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