Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis.

IF 3.2 3区医学 Q2 PSYCHIATRY

Frontiers in Psychiatry Pub Date : 2025-01-28 eCollection Date: 2024-01-01 DOI:10.3389/fpsyt.2024.1515549

Yi Pan, Pushi Wang, Bowen Xue, Yanbin Liu, Xinhua Shen, Shiliang Wang, Xing Wang

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引用次数: 0

Abstract

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.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.

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来源期刊

Frontiers in Psychiatry Medicine-Psychiatry and Mental Health

CiteScore

6.20

自引率

8.50%

发文量

2813

审稿时长

14 weeks

期刊介绍： Frontiers in Psychiatry publishes rigorously peer-reviewed research across a wide spectrum of translational, basic and clinical research. Field Chief Editor Stefan Borgwardt at the University of Basel is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. The journal''s mission is to use translational approaches to improve therapeutic options for mental illness and consequently to improve patient treatment outcomes.