Abstract
Biological age (BA) estimation from radiologic data is an important topic in clinical medicine, e.g. in determining endocrinological diseases or planning paediatric orthopaedic surgeries, while in legal medicine it is employed to approximate chronological age. In this work, we propose the use of deep convolutional neural networks (DCNN) for automatic BA estimation from hand MRI volumes, inspired by the way radiologists visually perform age estimation using established staging schemes that follow physical maturation. In our results we outperform the state of the art automatic BA estimation method, achieving a mean error between estimated and ground truth BA of \(0.36\,\pm \,0.30\) years, which is in line with radiologists doing visual BA estimation.
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