Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges

• Published in: Applied sciences Vol. 13; no. 4; p. 2302
• Main Authors: Hu, Mengjiao, Nardi, Cosimo, Zhang, Haihong, Ang, Kai-Keng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Algorithms; Attention deficit hyperactivity disorder; Autism; Brain; Consortia; Datasets; Deep learning; Diagnostic imaging; Empowerment; Hydrogen; Image processing; Machine learning; Magnetic fields; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Neural networks; Neurodevelopment; Neurodevelopmental disorders; Neuroimaging; pediatric; Pediatrics; Germany
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042302

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 neurodevelopment 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.