# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
###### Abstract  
Deep learning has achieved remarkable progress, particularly in neuroimaging analysis. Deep learning applications have also been extended from adult to pediatric medical images, and thus, this paper aims to present a systematic review of this recent research. We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. A non-exhaustive list of commonly used publicly available pediatric neuroimaging datasets and repositories are included, followed by a categorical review of recent works in pediatric MRI-based deep learning studies in the past five years. These works are categorized into recognizing neurodevelopmental disorders, identifying brain and tissue structures, estimating brain age/maturity, predicting neurodevelop outcomes, and optimizing MRI brain imaging and analysis. Finally, we also discuss the recent achievements and challenges on these applications of deep learning to pediatric neuroimaging.  
pediatric; magnetic resonance imaging; neurodevelopment; deep learning +
Footnote †: journal: Medical  
scanner. Then, an oscillating radio frequency deviates the magnetic momentum of the nuclei from the field. When the oscillating radio pulse is removed, signals generated by the realignment of hydrogen nuclei can be detected by a reciever coil [10; 11]. The most common MRI modality is the structural MRI (sMRI) which provides morphstructural information based on the concentration of hydrogen protons. sMRI measures the signals produced by aligned hydrogen protons in water molecules in the body and creates excellent contrast among different tissues. Functional MRI (fMRI) quantifies the blood oxygenation level-dependent (BOLD) signals based on the blood flow and blood oxygen changes around cells and reflects the brain activity information [12]. Resting-state fMRI (rs-fMRI) is measured when the subject is at rest while task fMRI monitors the brain function during an assigned task. Diffusion tensor imaging (DTI) estimates the motion of water molecules in the brain. The water molecules' diffusion speed and directions are restricted by tissue types and fiber architectures. DTI therefore provides information based on the quantitative anisotropy and orientation [13]. Deep learning methods have been widely applied to neuroimaging studies in adult for neuropsychiatric disorder recognition, brain tissues and structures segmentation, and clinical outcome prediction [8; 14; 15]. In comparison, relatively few deep learning studies have been conducted in pediatric MRI. Most previous reviews on pediatric MRI involved a large number of studies using conventional machine learning approaches instead of deep learning algorithms and some reviews focused on specific topics such as Autism [7; 16; 17]. To illustrate the most recent achievements of deep learning in pediatric MRI, this systematic review summarized the advanced deep learning approaches applied to multiple neurodevelopmental topics in MRI-based research in the past five years. Section 2 introduces the most commonly utilized deep learning algorithms as well as a list of available public datasets for neurodevelopment. Section 3 categorizes the recent studies into five main topics: recognizing neurodevelopmental disorders, identifying brain and tissue structures, estimating brain age/maturity, predicting neurodevelopment outcomes, and optimizing MRI brain imaging and analysis. The challenges and insights of applying deep learning to pediatric MRI are discussed in Section 4. We conclude in Section 5.