Abstract #2837

Estimating the Age of Healthy Children Based on Myelination Pattern in Brain MRI using a Deep Learning Neural Network Method

Yuya Saito^1,2, Akihiko Wada², Masaaki Hori², Koji Kamagata², and Shigeki Aoki²

¹Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, ²Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan

In this study, we used deep learning model to estimate the age of children based on the MR signal changes associated with myelination process on T1 and T2-weighed images. Brain MR images of 119 children age ranging from 0.25 to 24 months were first used as a training and test dataset. The age was then estimated by deep learning model based on the T1-WI and T2-WI dataset and T1-WI only dataset. Our results showed that convolution neural network model using T1WI and T2WI dataset demonstrated higher correlation and lower mean absolute error (MAE) compared to T1-WI only dataset.

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