Sun Bo, Cai Fei, Huang Huiman, Li Bo, Wei Bing
Department of Neonatology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Post-Graduate College, China Medical University, Shenyang, Liaoning, China.
Exp Biol Med (Maywood). 2025 Apr 24;250:10238. doi: 10.3389/ebm.2025.10238. eCollection 2025.
Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5%-7% of children worldwide. Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. This study aims to explore artificial intelligence models used for the prediction, early diagnosis and classification of attention deficit/hyperactivity disorder as reported in the literature. A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Out of the 1994 publications, 52 studies were included in the scoping review. The included articles reported the use of artificial intelligence for 3 different purposes. Of these included articles, artificial intelligence techniques were mostly used for the diagnosis of attention deficit/hyperactivity disorder (38/52, 79%). Magnetic resonance imaging (20/52, 38%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1,000 samples (28/52, 54%). Machine learning models were the most prominent branch of artificial intelligence used for attention deficit/hyperactivity disorder in the studies, and the support vector machine was the most used algorithm (34/52, 65%). The most commonly used validation in the studies was k-fold cross-validation (34/52, 65%). A higher level of accuracy (98.23%) was found in studies that used Convolutional Neural Networks algorithm. This review provides an overview of research on artificial intelligence models and algorithms for attention deficit/hyperactivity disorder, providing data for further research to support clinical decision-making in healthcare.
注意力缺陷多动障碍是一种常见的神经精神疾病，全球约5%-7%的儿童受其影响。人工智能提供了先进的模型和算法，以更好地诊断、预测和分类注意力缺陷多动障碍。本研究旨在探讨文献中报道的用于预测、早期诊断和分类注意力缺陷多动障碍的人工智能模型。按照PRISMA-ScR（系统评价和Meta分析扩展版的范围综述优先报告项目）指南进行了范围综述并报告结果。在1994篇出版物中，有52项研究纳入了范围综述。纳入的文章报道了人工智能用于3种不同目的。在这些纳入的文章中，人工智能技术大多用于注意力缺陷多动障碍的诊断（38/52，79%）。磁共振成像（20/52，38%）是纳入文章中最常使用的数据。大多数纳入文章使用的数据集样本量小于1000（28/52，54%）。机器学习模型是研究中用于注意力缺陷多动障碍的人工智能最突出的分支，支持向量机是最常用的算法（34/52，65%）。研究中最常用的验证方法是k折交叉验证（34/52，65%）。使用卷积神经网络算法的研究中发现了更高水平的准确率（98.23%）。本综述概述了关于注意力缺陷多动障碍的人工智能模型和算法的研究，为进一步研究提供数据，以支持医疗保健中的临床决策。
