Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis

作者全名：Tian, Lu; Zheng, Helin; Zhang, Ke; Qiu, Jiawen; Song, Xuejuan; Li, Siwei; Zeng, Zhao; Ran, Baosheng; Deng, Xin; Cai, Jinhua

作者地址：[Tian, Lu; Zheng, Helin; Zhang, Ke; Qiu, Jiawen; Song, Xuejuan; Li, Siwei; Zeng, Zhao; Ran, Baosheng; Deng, Xin; Cai, Jinhua] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp, Minist Educ,Key Lab Child Dev & Disorders,Chongqin, Chongqing 400014, Peoples R China

通信作者：Cai, JH (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Radiol,Childrens Hosp, Minist Educ,Key Lab Child Dev & Disorders,Chongqin, Chongqing 400014, Peoples R China.

来源：JOURNAL OF AFFECTIVE DISORDERS

ESI学科分类：PSYCHIATRY/PSYCHOLOGY

WOS号：WOS:001224789500001

JCR分区：Q1

影响因子：4.9

年份：2024

卷号：355

期号：　

开始页：459

结束页：469

文献类型：Review

关键词：Structural magnetic resonance imaging; Functional magnetic resonance imaging; Machine learning; Attention-deficit/hyperactivity disorder; Systematic review; Meta-analysis

摘要：Background: The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. Methods: We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. Results: Forty-three studies were included in the systematic review, 27 of which were included in our metaanalysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD. Limitations: Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data. Conclusion: sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.

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