Automated age estimation of young individuals based on 3D knee MRI using deep learning
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Automated age estimation of young individuals based on 3D knee MRI using deep learning. / Auf der Mauer, Markus ; Well, Eilin Jopp-van; Herrmann, Jochen; Groth, Michael; Morlock, Michael M; Maas, Rainer; Säring, Dennis.
in: INT J LEGAL MED, Jahrgang 135, Nr. 2, 03.2021, S. 649-663.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Automated age estimation of young individuals based on 3D knee MRI using deep learning
AU - Auf der Mauer, Markus
AU - Well, Eilin Jopp-van
AU - Herrmann, Jochen
AU - Groth, Michael
AU - Morlock, Michael M
AU - Maas, Rainer
AU - Säring, Dennis
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
U2 - 10.1007/s00414-020-02465-z
DO - 10.1007/s00414-020-02465-z
M3 - SCORING: Journal article
C2 - 33331995
VL - 135
SP - 649
EP - 663
JO - INT J LEGAL MED
JF - INT J LEGAL MED
SN - 0937-9827
IS - 2
ER -