精神分裂症中的动态面部表情识别缺陷:一个分类研究
Nan Chen1, Yuna She1, Jingkai Jiang1
1Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
Asian journal of psychiatry
|October 25, 2025
概括
精神分裂症患者表现出明显的面部表情识别缺陷,影响社会功能. 基于这些缺陷开发的机器学习模型实现了高精度,显示了早期精神分裂症查的潜力.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 精神分裂症的特征是稳定,多维的面部表情识别缺陷影响社会功能.
- 目前的精神分裂症早期查方法在范围和有效性方面有限.
研究的目的:
- 研究精神分裂症患者的面部表情识别缺陷.
- 开发基于机器学习的歧视模型,用于早期精神分裂症查和精确治疗.
主要方法:
- 51名精神分裂症患者和62名健康对照人进行了情绪识别任务,包括改变面部表情.
- 参与者确定了情绪;临床症状使用标准化尺度 (例如,PANSS,SANS,SAPS,BPRS) 进行评估.
主要成果:
- 精神分裂症患者在识别特定情绪 (恐惧,厌恶,愤怒,快乐,悲伤) 时表现出较低的准确性和敏感性.
- 患者经常混负面情绪和幸福感;症状严重程度与识别能力有负相关性.
- 查模型实现了79%的准确性和0.84的AUC,具有88%的敏感性和89%的特异性.
结论:
- 精神分裂症患者在面部表情识别准确性,错误模式和敏感性方面存在显著的,与症状严重程度相关的缺陷.
- 开发的查模型显示出高AUC和灵敏度,表明其作为早期精神分裂症检测的方便辅助工具的潜力.
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