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SYSTEMATIC REVIEW article

Front. Psychiatry
Sec. Mood Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1515549

Machine Learning for the Diagnosis Accuracy of Bipolar Disorder: A Systematic Review and Meta-Analysis

Provisionally accepted
Yi Pan Yi Pan 1Pushi Wang Pushi Wang 2*Bowen Xue Bowen Xue 3*Yanbin Liu Yanbin Liu 2*Xinhua Shen Xinhua Shen 1Shiliang Wang Shiliang Wang 1*Xing Wang Xing Wang 4*
  • 1 Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China, Huzhou, China
  • 2 National Center for Mental Health ,China NCMHC, Huzhou, China
  • 3 Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310007, Zhejiang, China, Huzhou, China
  • 4 Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, China

The final, formatted version of the article will be published soon.

    Background: 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. Methods: 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. Results: 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 SROC curve 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). Conclusions: 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.

    Keywords: Depression, Bipolar Disorder, machine learning, predictive model, Systematic review

    Received: 23 Oct 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 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) or licensor 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.

    * Correspondence:
    Pushi Wang, National Center for Mental Health ,China NCMHC, Huzhou, China
    Bowen Xue, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310007, Zhejiang, China, Huzhou, China
    Yanbin Liu, National Center for Mental Health ,China NCMHC, Huzhou, China
    Shiliang Wang, Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China, Huzhou, China
    Xing Wang, Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, China

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