整合人工智能驱动的技术和面部语义特征用于抑郁症检测:一个横截面研究
Mei-Feng Lin1, Yi-Chien Pan1, Fei-Pi Liu1
1Department of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Journal of affective disorders
|December 20, 2025
概括
人工智能 (AI) 系统在检测门诊患者抑郁症方面表现有前途. iSeeME面部表情模型表现出比EDDTW-V2系统更高的准确性和与抑郁度尺度的相关性.
科学领域:
- 人工智能的人工智能
- 精神病学是一个精神病学.
- 数字健康数字健康
背景情况:
- 抑郁症是一个全球性的健康问题,在门诊机构的早期检测方面存在挑战.
- 限制包括自我报告偏见,耻辱和低报告抑郁症状.
- 人工智能为早期抑郁症检测提供客观,可扩展和不引人注目的方法.
研究的目的:
- 评估两个人工智能系统iSeeME和EDDTW-V2的预测准确度,用于识别抑郁症状.
- 评估AI在高风险门诊患者中检测抑郁症的实用性.
- 为了比较面部表情分析 (iSeeME) 和叙事分析 (EDDTW-V2) 的性能.
主要方法:
- 一项涉及62名精神病学和外科瘤诊所门诊患者的横截面研究.
- 进行了标准化的抑郁评估 (HDRS,BDI-II,PHQ-9).
- 面部表情数据由iSeeME分析;叙事转录由EDDTW-V2;通过指标,相关性和集群分析评估的预测有效性.
主要成果:
- 在62名参与者中,有39人患有临床抑郁症 (HDRS≥7).
- iSeeME实现了0.761的精度,0.854的回忆,0.805的F1得分和0.770的准确性.
- iSeeME与BDI-II (r=0.442) 和PHQ-9 (r=0.335) 的相关性比EDDTW-V2.2更强.
结论:
- 这两种人工智能系统都显示出作为抑郁症评估的补充工具的潜力.
- iSeeME擅长检测情感行为症状;EDDTW-V2捕获认知语言特征.
- 人工智能支持早期检测,监测和针对性干预,在门诊环境下治疗抑郁症.
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