From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).

IF 1.6 4区心理学 Q4 CLINICAL NEUROLOGY

Applied Neuropsychology-Adult Pub Date : 2026-08-01 DOI:10.1080/23279095.2026.2707490

Mahi Khemchandani, Manjusha Pradeep Deshmukh

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引用次数: 0

Abstract

Background: The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI).

Objective: This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions.

Methods: A structured search of the Scopus database retrieved 722 publications from 2011 to 2024. Bibliometric indicators were analyzed using Python and VOSviewer to evaluate annual production, geographical distribution, and technological trends.

Results: The field has entered an exponential growth phase, with publication output peaking in 2023. A geopolitical analysis reveals a "research duopoly" between the United States (130 papers) and China (129 papers). Technologically, there is a distinct transition from traditional ML-support vector machines (SVM) to deep learning (DL) architectures. Crucially, electroencephalography (EEG) has emerged as the preferred neuroimaging modality over functional magnetic resonance imaging (fMRI) in recent AI studies, driven by its cost-effectiveness and high temporal resolution.

Conclusion: AI-based ADHD diagnosis has matured from an exploratory niche to an evolving domain of computational psychiatry. Future research must prioritize explainable AI (XAI) and multimodal data fusion to translate high algorithmic accuracy into clinical utility.

查看原文本刊更多论文

从机器学习到深度学习在注意力缺陷多动障碍诊断中的应用：全球趋势的文献计量学分析（2011-2024）。

背景：注意缺陷多动障碍（ADHD）的诊断传统上依赖于主观的临床访谈。近年来，在人工智能（AI）的驱动下，人们开始转向客观、数据驱动的诊断模式。目的：本研究对人工智能和机器学习（ML）在ADHD预测中的应用进行了全面的文献计量分析，以描绘该领域的演变、当前趋势和未来方向。方法：对Scopus数据库进行结构化检索，检索2011 - 2024年出版的文献722篇。使用Python和VOSviewer对文献计量指标进行分析，以评估年产量、地理分布和技术趋势。结果：该领域已进入指数增长阶段，出版物产量在2023年达到峰值。地缘政治分析揭示了美国（130篇论文）和中国（129篇论文）之间的“研究双头垄断”。从技术上讲，从传统的机器学习支持向量机（SVM）到深度学习（DL）架构有一个明显的过渡。至关重要的是，在最近的人工智能研究中，由于其成本效益和高时间分辨率，脑电图（EEG）已成为优于功能磁共振成像（fMRI）的首选神经成像方式。结论：基于人工智能的ADHD诊断已经从一个探索性的小生境发展成为计算精神病学的一个不断发展的领域。未来的研究必须优先考虑可解释的人工智能（XAI）和多模式数据融合，以将高算法准确性转化为临床应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Neuropsychology-Adult CLINICAL NEUROLOGY-PSYCHOLOGY

CiteScore

4.50

自引率

11.80%

发文量

134

期刊介绍： pplied Neuropsychology-Adult publishes clinical neuropsychological articles concerning assessment, brain functioning and neuroimaging, neuropsychological treatment, and rehabilitation in adults. Full-length articles and brief communications are included. Case studies of adult patients carefully assessing the nature, course, or treatment of clinical neuropsychological dysfunctions in the context of scientific literature, are suitable. Review manuscripts addressing critical issues are encouraged. Preference is given to papers of clinical relevance to others in the field. All submitted manuscripts are subject to initial appraisal by the Editor-in-Chief, and, if found suitable for further considerations are peer reviewed by independent, anonymous expert referees. All peer review is single-blind and submission is online via ScholarOne Manuscripts.