人工智能辅助的多模式信息用于查抑郁症:系统性审查和元分析
Luyao Wang1, Chenhan Wang1, Chenyang Li1
1Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
NPJ digital medicine
|August 16, 2025
概括
人工智能 (AI) 显示出使用生理和行为数据进行抑郁查的前景. 人工智能辅助的多模式方法显著优于单一方法,深度学习模型显示出高精度.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗人工智能的人工智能
- 心理健康诊断 心理健康诊断 心理健康诊断
背景情况:
- 抑郁症是一种广泛且昂贵的心理健康障碍,影响所有年龄组.
- 早期和准确的抑郁症查对于及时干预和改善患者结果至关重要.
- 传统的抑郁查方法可能是主观的,耗时的.
研究的目的:
- 系统地审查用于抑郁查的人工智能 (AI) 辅助生理和行为测量的分类性能.
- 在抑郁症检测中比较多式模式与单式模式人工智能方法的有效性.
- 评估AI辅助抑郁查中的深度学习模型的性能.
主要方法:
- 在主要的学术数据库 (谷歌学者,科学网,IEEE Xplore) 进行了全面的文献搜索,截至2025年6月7日.
- 包括报告分类绩效指标的符合条件的研究.
- 聚合的AUC (曲线下的面积) 值被计算出来,以估计不同AI方法的性能.
主要成果:
- 人工智能辅助的多模式方法实现了0.95 (95% CI:0.92-0.96) 的AUC,优于单模式方法 (AUC:0.84-0.92).
- 深度学习模型,AI的一个子集,表现出高性能,AUC为0.95 (95%CI:0.93-0.97).
- 这些发现表明,人工智能在提高抑郁症查准确度方面具有显著的潜力.
结论:
- 基于人工智能的多模式信息为客观和高效的抑郁查提供了强大的工具.
- 深度学习模型在提高人工智能辅助抑郁症检测的诊断准确度方面特别有希望.
- 未来的研究应该专注于建立标准化数据库和完善研究设计,以进一步验证这些AI方法.
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