  Human brain development is rapid during infancy and early childhood. Many
disease processes impair this development. Therefore, brain developmental age
estimation (BDAE) is essential for all diseases affecting cognitive
development. Brain magnetic resonance imaging (MRI) of infants shows brain
growth and morphologic patterns during childhood. Therefore, we can estimate
the developmental age from brain images. However, MRI analysis is
time-consuming because each scan contains millions of data points (voxels). We
investigated the three-dimensional convolutional neural network (3D CNN), a
deep learning algorithm, to rapidly classify neurodevelopmental age with high
accuracy based on MRIs. MRIs from normal newborns were obtained from the
National Institute of Mental Health (NIMH) Data Archive. Age categories of
pediatric MRIs were 3 wks + 1 wk, 1 yr + 2 wks, and 3 yrs + 4 wks. We trained a
BDAE method using T1, T2, and proton density (PD) images from MRI scans of 112
individuals using 3D CNN. Compared with the known age, our method has a
sensitivity of 99% and specificity of 98.3%. Moreover, our 3D CNN model has
better performance in neurodevelopmental age estimation than does 2D CNN.
