自动语音分析在检测抑郁症方面的表现:系统审查和元分析.
Patricia Laura Maran1,2, María Dolores Braquehais1,3,4,5, Alexandra Vlaic6
1Psychiatry, Mental Health and Addictions Group, Vall d'Hebron Research Institute (VHIR), Instituto de Investigación Sanitaria Acreditado Instituto de Investigación - Hospital Universitario Vall d'Hebron (IR-HUVH), Barcelona, Catalonia, Spain.
JMIR mental health
|October 22, 2025
概括
自动语音分析 (ASA) 显示了抑郁症检测的前景,总准确率约为81%. 然而,目前最好将其用作补充工具,而不是独立的诊断方法.
科学领域:
- 心理健康 心理健康
- 人工智能的人工智能
- 语音分析 语言分析
背景情况:
- 抑郁症很普遍,而且经常被诊断不足.
- 自动语音分析 (ASA) 为抑郁症评估提供了一个潜在的解决方案.
- 需要对ASA诊断准确性的全面评估.
研究的目的:
- 系统地审查和元分析ASA用于抑郁症检测的诊断性能.
- 评估ASA中的机器学习和深度学习方法,以评估抑郁症.
主要方法:
- 对8个数据库 (2013年1月至2025年4月) 进行系统搜索,以获得有关抑郁症ASA的英语研究.
- 包括报告诊断准确度指标的研究.
- 使用修改后的 QUADAS-R. 的质量评估.
- 三级元分析以估计聚合的准确性,灵敏度,特异性和精度.
- 进行元回归和子组分析以探索异质性.
主要成果:
- 105项研究符合1345个记录中的纳入标准.
- 聚合的最高准确率为:0.81 (95% CI 0.79-0.83).
- 聚合的最高灵敏度:0.84 (95%CI为0.81-0.86).
- 聚合的最高特异性:0.83 (95%CI为0.79-0.86).
- 集中的最高精度:0.81 (95% CI 0.77-0.84).
- 聚合最低的准确率:0.66 (95% CI 0.63-0.69). 在这个数据库中,最低的准确率是:0.66 (95% CI 0.63-0.69).
结论:
- ASA显示了抑郁症检测的潜力.
- 作为独立工具的临床应用目前是有限的.
- 在各种环境中,ASA最好被用作一种互补的方法.
- 需要进一步的高质量研究,以获得强大的和可通用的模型.
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