The brain undergoes various developmental stages, from neurogenesis to myelination, which significantly impact brain connectivity. The history of neuroscience highlights the opposing theories of Golgi and Cajal, focusing on how neurons communicate. Advances in technology, such as MRI, have revolutionized the study of brain structure and function. The emergence of deep learning and machine learning has further enhanced our understanding of neurological disorders in children. Deep learning, inspired by the brain’s learning process, has shown promise in diagnosing and managing neurological conditions in children. Challenges in pediatric neuroimaging studies include data volume and quality, as well as methodological constraints. Overcoming these challenges requires careful implementation and selection of deep learning architectures. Studies have shown the potential of deep learning in various classification tasks related to children’s neurological disorders. Future research should focus on conducting multi-site validation studies and benchmark evaluations to enhance the clinical efficacy of deep learning in pediatric neuroimaging. Deep learning offers a promising avenue for understanding and treating neurological disorders in children, potentially revolutionizing the field of pediatric neuroimaging.
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Source link: https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2021.670489/full
Can Deep Learning Study Neurological Disorders in Children Effectively? #NeurologicalDisorders
