[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122200-en":3,"doc-seo-122200-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122200,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning for the diagnosis accuracy of bipolar disorder - a systematic review and meta-analysis","This systematic review evaluates the diagnostic value of machine learning for bipolar disorder, addressing ongoing controversy about accuracy. Searches across PubMed, Embase, Cochrane, and Web of Science (to April 1, 2023) included 18 studies with 3152 participants (1858 bipolar disorder cases) and 28 machine learning models. The review reports high discrimination performance versus normal individuals and versus depression, while noting limited clinical progress due to predominately binary classification.","TYPE Systematic Review PUBLISHED 28 January 2025 DOI 10.3389/fpsyt.2024.1515549  \nOPEN ACCESS  \nEDITED BY  \nTrine Vik Lagerberg,  \nOslo University Hospital, Norway  \nREVIEWED BY  \nMariusz Stanisław Wiglusz,  \nMedical University of Gdansk, Poland Massimo Tusconi,  \nUniversity of Cagliari, Italy  \n*CORRESPONDENCE  \nShiliang Wang  \n [wangsl1177@hz3rd-hosp.cn](wangsl1177@hz3rd-hosp.cn)[ ](wangsl1177@hz3rd-hosp.cn)Xing Wang  \n [Wonder@hz3rd-hosp.cn](Wonder@hz3rd-hosp.cn)  \nRECEIVED 23 October 2024  \nACCEPTED 20 December 2024  \nPUBLISHED 28 January 2025  \nCITATION  \nPan Y, Wang P, Xue B, Liu Y, Shen X, Wang Sand Wang X (2025) Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.  \nFront. Psychiatry 15:1515549 .  \ndoi: 10.3389/fpsyt.2024.1515549  \nCOPYRIGHT  \n© 2025 Pan, Wang, Xue, Liu, Shen, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis  \nYi Pan 1, Pushi Wang 2, Bowen Xue 3, Yanbin Liu 2, Xinhua Shen 1, Shiliang Wang 1* and Xing Wang 1*  \n1 Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China, 2 Department of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, China, 3Affiliated Mental Health Center & Hangzhou Seventh People ’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China  \nBackground: Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.  \nMethods: We searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023 . QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis.  \nResults: 18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROCcurve was 0 .94(95% CI: 0 .92~0 .96) . The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0 .84 (95%CI: 0 .80~0 .87) and 0 .82 (95%CI: 0 .75~0 .88) respectively. The SROC curve was 0 .89 (95%CI: 0 .86~0 .91) .  \nConclusions: Machine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods.  \nSystematic review registration: [https://www.crd.york.ac.uk/prospero/display_](https://www.crd.york.ac.uk/prospero/display_)[record.php?ID=CRD42023427290](record.php?ID=CRD42023427290), [identi](identi)fi[er CRD42023427290](er CRD420","cbCaidboUa31lqta","https://ap.wps.com/l/cbCaidboUa31lqta","pdf",2952485,1,12,"English","en",105,"# Background\n## Diagnostic challenges in bipolar disorder\n# Methods\n## Literature search strategy\n## Quality assessment and meta-analysis approach\n# Results\n## Included studies and model coverage\n## Diagnostic performance versus normal individuals\n## Diagnostic performance versus depression\n# Conclusions","[{\"question\":\"What question does the systematic review address?\",\"answer\":\"It assesses the diagnostic value and accuracy of machine learning methods for bipolar disorder.\"},{\"question\":\"How was the literature searched and evaluated?\",\"answer\":\"The review searched PubMed, Embase, Cochrane, and Web of Science up to April 1, 2023, and applied QUADAS-2 for quality assessment, followed by a bivariate mixed-effects meta-analysis.\"},{\"question\":\"What does the review find about diagnostic performance?\",\"answer\":\"Machine learning methods show high sensitivity and specificity for distinguishing bipolar disorder from normal individuals and from depression, with strong SROC values reported for both comparisons.\"}]","Machine learning for the diagnosis accuracy of bipolar disorder - 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