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.
CITATION STYLE
Štern, D., Kainz, P., Payer, C., & Urschler, M. (2017). Multi-factorial age estimation from skeletal and dental MRI volumes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10541 LNCS, pp. 61–69). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_8
Mendeley helps you to discover research relevant for your work.