用眼动追踪+AI分析注意力模式，快速筛查抑郁与社交焦虑
AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study
- 通过卷积神经网络分析眼动轨迹生成的图像
- 两分类准确率达62%，三分类平均48%
- 适合用于便捷、自然环境下的心理健康初筛
幸福感是动态变化的，其量化困难。心理困扰常伴随对特定刺激（如人脸）的视觉注意偏倚。本文提出一种基于AI的眼动追踪筛查方法，利用残差卷积神经网络（ResNet）分析由眼动路径生成的图像。数据来自两项研究：一项针对重度抑郁症患者，另一项针对社交焦虑个体。实验结果表明，该方法在三分类系统中平均准确率为48%，在两分类系统中达到62%。基于这些初步发现，该方法有望应用于快速、生态化且高效的 mental health screening 系统，实现通过眼动追踪评估个体幸福感。
原文摘要 · Abstract (English)
Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduced well-being is often linked to depression or anxiety disorders, which are characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-assisted screening of affective disorders by analysing visual attention scan paths using convolutional neural networks (CNNs). Data were collected from two studies examining (1) attentional tendencies in individuals diagnosed with major depression and (2) social anxiety. These data were processed using residual CNNs through images generated from eye-gaze patterns. Experimental results, obtained with ResNet architectures, demonstrated an average accuracy of 48% for a three-class system and 62% for a two-class system. Based on these exploratory findings, we propose that this method could be employed in rapid, ecological, and effective mental health screening systems to assess well-being through eye-tracking.
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