Application of machine learning and deep learning in the diagnosis of attention deficit hyperactivity disorder

TAN Jing; CHEN Li

doi:10.11852/zgetbjzz2024-0510

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Chinese Journal of Child Health Care ›› 2025, Vol. 33 ›› Issue (4) : 426-430. DOI: 10.11852/zgetbjzz2024-0510

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Application of machine learning and deep learning in the diagnosis of attention deficit hyperactivity disorder

TAN Jing, CHEN Li

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Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children, which lacks objective tests and is difficult to diagnose. This paper reviews the application of machine learning and deep learning in ADHD diagnosis and the value of assisted diagnosis. Research in this field focuses on several key areas: text analysis, electrophysiological testing, neuroimaging, behavioral data analysis, genomics, and multi-omics studies. Despite the significant potential demonstrated by machine learning and deep learning in diagnosing ADHD, challenges such as data quality, overfitting, model interpretability, and ethical issues remain. Future research needs to be focused in the areas of improving algorithm performance, enhancing model interpretability, ensuring data privacy and security, and validating on a broader range of clinical samples.

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attention deficit hyperactivity disorder / diagnosis / machine learning / deep learning

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TAN Jing, CHEN Li. Application of machine learning and deep learning in the diagnosis of attention deficit hyperactivity disorder[J]. Chinese Journal of Child Health Care. 2025, 33(4): 426-430 https://doi.org/10.11852/zgetbjzz2024-0510

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