Abstract
Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of \(1.14 \pm 0.96\) years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.
D. Štern—This work was supported by the Austrian Science Fund (FWF): P 28078-N33.
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Y. Jia, GitHub repository, https://github.com/BVLC/caffe/.
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Štern, D., Kainz, P., Payer, C., Urschler, M. (2017). Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_8
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