Reliability and validity of facial expression recognition in the screening of generalized anxiety disorder

广泛性焦虑症 可靠性(半导体) 心理学 临床心理学 面部表情 焦虑 有效性 焦虑症 心理测量学 模式识别(心理学) 认知心理学 精神科 沟通 量子力学 物理 功率(物理)
作者
Xueqing Ren,Shanshan Su,Wenhui Jiang,Yuan Wang,Jiayu Yao,Yousong Su,Yanru Wu,Jing Tao,Yihua Peng,Jianyin Qiu
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:369: 538-546 被引量:9
标识
DOI:10.1016/j.jad.2024.10.022
摘要

Anxiety disorder is one of the most prevalent mental disorders in China. However, there are obvious subjective factors in the current assessment of anxiety disorders, which may lead to certain diagnostic errors. The identification and diagnosis of anxiety disorders can be further improved if objective biological indicators are added in the assessment process. The current research validates facial expression recognition as a screening tool to assist in detecting generalized anxiety disorder. Based on the International Affective Picture System, we constructed an aided diagnostic experimental paradigm and recorded their facial expression. The split-half reliability was displayed by the Pearson correlation heatmap. The paradigm, GAD-7 and HAMA scales were administered to 60 generalized anxiety disorder patients and 60 matched healthy controls to evaluate the criterion-related validity. Additionally, we conducted a diagnostic study by using MINI as a gold standard and calculated ROC analysis to examine the screening performance of the facial expressions. The heatmap showed very high correlations ( r > 0.60, PS < 0.05) along the diagonal of the square heatmap (from the bottom left corner to the top right). The Pearson correlation coefficients between the GAD-7, HAMA and seven facial expressions ranged from −0.35(neutral, P < 0.01) to 0.34(angry, P < 0.01). The intergroup effects of neutral, anger and fear emotions were statistically significant ( F = 18.893, P < 0.001; F = 20.535, P < 0.001; F = 9.091, P = 0.003). ROC analysis showed AUC for neutral, angry and scared facial expressions were 0.723, 0.792 and 0.727 respectively. This study constructed a tool for auxiliary screening of GAD patients and provided an objective automatic facial expression recognition method to assist psychological diagnosis. • This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, combined with facial expression recognition technology to assist in screening people with GAD. • The heatmap showed satisfactory split-half reliability. • The criterion-related validity and screening validity were excellent. • Three facial expressions are significant between GAD and HC: neutral, angry and scared facial expressions.
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