  The ability to determine if the brain is developing normally is a key
component of pediatric neuroradiology and neurology. Brain magnetic resonance
imaging (MRI) of infants demonstrates a specific pattern of development beyond
simply myelination. While radiologists have used myelination patterns, brain
morphology and size characteristics in determining if brain maturity matches
the chronological age of the patient, this requires years of experience with
pediatric neuroradiology. Due to the lack of standardized criteria, estimation
of brain maturity before age three remains fraught with interobserver and
intraobserver variability. An objective measure of brain developmental age
estimation (BDAE) could be a useful tool in helping physicians identify
developmental delay as well as other neurological diseases. We investigated a
three-dimensional convolutional neural network (3D CNN) to rapidly classify
brain developmental age using common MRI sequences. MRI datasets from normal
newborns were obtained from the National Institute of Mental Health Data
Archive from birth to 3 years. We developed a BDAE method using T1-weighted, as
well as a fusion of T1-weighted, T2-weighted, and proton density (PD) sequences
from 112 individual subjects using 3D CNN. We achieved a precision of 94.8% and
a recall of 93.5% in utilizing multiple MRI sequences in determining BDAE.
