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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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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机器学习诊断双相情感障碍的准确性：系统回顾和荟萃分析。

背景：诊断双相情感障碍在临床实践中提出了挑战，需要大量的时间投入。随着人工智能在心理健康领域的应用越来越广泛，研究人员正在努力建立基于人工智能的诊断模型。在这种背景下，一些研究人员试图开发双相情感障碍诊断的机器学习模型。然而，这些诊断的准确性仍然是一个有争议的话题。因此，我们进行了这项系统综述，以全面评估机器学习在双相情感障碍背景下的诊断价值。方法：检索PubMed、Embase、Cochrane和Web of Science，检索截止日期为2023年4月1日。采用QUADAS-2评价纳入文献的质量。此外，我们采用双变量混合效应模型进行meta分析。结果：纳入18项研究，涵盖3152名参与者，其中包括1858例双相情感障碍。包含28个机器学习模型。鉴别双相情感障碍与正常人的敏感性和特异性分别为0.88 （9.5% CI: 0.74~0.95）和0.89 (95% CI: 0.73~0.96)， SROC曲线为0.94（95% CI: 0.92~0.96）。鉴别双相情感障碍与抑郁症的敏感性和特异性分别为0.84 （95%CI: 0.80~0.87）和0.82 （95%CI: 0.75~0.88）。SROC曲线为0.89 （95%CI: 0.86~0.91）。结论：机器学习方法可用于双相情感障碍的鉴别和诊断。然而，在目前的研究中，它们主要用于二元分类任务，限制了它们在临床实践中的进展。因此，在未来的研究中，我们期望开发更多的多类分类任务，以提高这些方法的临床适用性。系统综述注册：https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290，标识符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.