Role of Deep Learning in Neurodevelopmental Disorders: A Narrative Review
Year: 2025
Type: review
Abstract: The application of Deep Learning (DL) techniques in paediatric neuroimaging marks a significant milestone in the field. Using various DL methods, this study aims to provide a review of how these techniques can improve the diagnostic process for different neurodevelopmental conditions, including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). The subsequent discussion addresses the prominent DL approaches applicable to paediatric neuroimaging and describes the key datasets that serve as the foundation of scientific research in this area. Additionally, the study highlights the limitations and shortcomings of these techniques, along with potential directions for future research and opportunities for further development. The adoption of these advanced methods has the potential to significantly improve patient outcomes, enhance diagnostic accuracy, and advance our understanding of early brain development.
Authors: Nilah Ans Chacko, Nimmi Pradeep, Abhinav Panayan, Sayali Satish Chodankar
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $275
OpenAlex ID: W7092208047
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