Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis
BackgroundDiagnosing 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 s...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1515549/full |
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| author | Yi Pan Pushi Wang Bowen Xue Yanbin Liu Xinhua Shen Shiliang Wang Xing Wang |
| author_facet | Yi Pan Pushi Wang Bowen Xue Yanbin Liu Xinhua Shen Shiliang Wang Xing Wang |
| author_sort | Yi Pan |
| collection | DOAJ |
| description | BackgroundDiagnosing 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.MethodsWe 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.Results18 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).ConclusionsMachine 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 registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290. |
| format | Article |
| id | doaj-art-dcf95e2b679c458395f28cd4b5b8e45a |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-dcf95e2b679c458395f28cd4b5b8e45a2025-01-28T06:41:09ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011510.3389/fpsyt.2024.15155491515549Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysisYi Pan0Pushi Wang1Bowen Xue2Yanbin Liu3Xinhua Shen4Shiliang Wang5Xing Wang6Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, ChinaDepartment of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, ChinaAffiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, ChinaDepartment of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, ChinaDepartment of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, ChinaDepartment of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, ChinaBackgroundDiagnosing 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.MethodsWe 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.Results18 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).ConclusionsMachine 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 registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1515549/fulldepressionbipolar disordermachine learningpredictive modelsystematic review |
| spellingShingle | Yi Pan Pushi Wang Bowen Xue Yanbin Liu Xinhua Shen Shiliang Wang Xing Wang Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis Frontiers in Psychiatry depression bipolar disorder machine learning predictive model systematic review |
| title | Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis |
| title_full | Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis |
| title_fullStr | Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis |
| title_full_unstemmed | Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis |
| title_short | Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis |
| title_sort | machine learning for the diagnosis accuracy of bipolar disorder a systematic review and meta analysis |
| topic | depression bipolar disorder machine learning predictive model systematic review |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1515549/full |
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