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

TL;DR

This study develops a 3D CNN model to accurately classify neurodevelopmental age in infants from MRI scans, significantly improving speed and accuracy over traditional methods.

Contribution

The paper introduces a novel 3D CNN approach for rapid and precise neurodevelopmental age estimation from infant brain MRI, outperforming 2D CNN models.

Findings

Achieved 99% sensitivity and 98.3% specificity in age classification.

Demonstrated superior performance of 3D CNN over 2D CNN.

Validated method on MRI data from 112 infants.

Abstract

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.