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使用卷积神经网络进行面部表情分析,用于无毒和慢性精神分裂症

Tongxin Li¹, Xiaofei Zhang², Conghui Wang³

¹Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China; Brain Assessment & Intervention Laboratory, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China.

Journal of psychiatric research

|December 5, 2024

概括

使用卷积神经网络 (CNN) 的面部图像分析可以准确检测精神分裂症患者. 无毒精神分裂症患者的特定面部表情与精神病症状相关,表明潜在的诊断指标.

科学领域:

计算精神病学是一种计算精神病学.
机器学习在心理健康中的应用
面部表情分析的方法

背景情况:

面部图像可以反映心理健康状况.
精神分裂症的诊断通常依赖于临床面试和症状评估.
需要用于诊断精神分裂症的客观标志物.

研究的目的:

使用面部图像来区分无药性精神分裂症 (DN-SCZ),慢性精神分裂症 (C-SCZ) 和健康对照 (HCs).
调查这三组人群中面部表情的差异.
探索精神分裂症患者的面部表情和精神症状之间的关系.

主要方法:

招募了45个DN-SCZ,106个C-SCZ和101个HC.
使用卷积神经网络 (CNN) 进行群组分类 (90.99%准确度) 和面部表情预测 (准确度高达95.95%).
通过积极和消极综合征尺度评估精神症状.

主要成果:

美国有线电视新闻网准确地分类了参与者,并预测了基本的面部表情.
与DN/C-SCZ患者相比,HC患者的面部图像在价值,兴奋和吸引力方面被评为更高.
特定的面部表情 (例如,悲伤,惊,快乐,愤怒) 在不同组之间有显著差异,并且与DN-SCZ患者的症状得分相关.

结论:

面部表情显示了精神分裂症患者和健康对照人群之间的客观差异.
面部表情分析有可能作为检测DN-SCZ和C-SCZ的指示信号.
在DN-SCZ患者中,某些面部表情与他们的精神症状有关.

关键词:

慢性精神分裂症慢性精神分裂症卷积神经网络是一种卷积神经网络.药物原始精神分裂症.面部表情面部表情面部图像面部图像面部图像

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