Neurodevelopmental Age Estimation of Infants Using a 3D-Convolutional Neural Network Model based on Fusion MRI Sequences

2020/10/07 by M. Shabanian, Mahdieh Shabanian, Shabanian, M. +8 · 1 citation
Computer Science · Engineering · Medicine · Neuroscience · Psychology · #Artificial intelligence #Computer science #Convolutional neural network #Fetal and Pediatric Neurological Disorders #Magnetic resonance imaging #Medicine #Neonatal Respiratory Health Research #Neonatal and fetal brain pathology #Neuroimaging #Neurology #Neuroradiology #Neuroscience #Psychology #Radiology #cs.LG #eess.IV #msc:68T07

paper · pdf · doi:10.48550/arxiv.2010.03963

published in arXiv (Cornell University) (Cornell University) · 18 pages, 8 figures, 4 tables. Supplementary information: 7 figures, 3 tables

arxiv created 2020/10/07 · openalex publication_date 2020/10/07 · arxiv updated 2020/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

Citations

• ImageNet classification with deep convolutional neural networks

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