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

Classification of Neurodevelopmental Age in Normal Infants Using 3D-CNN based on Brain MRI