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AI Identifies Subtle Signs of Depression in Student Facial Expressions

AI detects subtle facial signs of depression in students, aiding early intervention and mental health monitoring. πŸ€–πŸ˜Ÿ

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AI Identifies Subtle Signs of Depression in Student Facial Expressions

Overview

Depression is a prevalent mental health issue, yet its early indicators are frequently missed. Research indicates that reduced facial expressivity may be a sign of depression. A recent study conducted by Waseda University in Japan has explored the connection between subthreshold depression (StD) and changes in facial expressions among students.

Research Details

Associate Professor Eriko Sugimori and doctoral student Mayu Yamaguchi analyzed facial expressions of Japanese undergraduates using artificial intelligence. The findings were published in the journal Scientific Reports on August 21, 2025.

Methodology
  • 64 Japanese university students recorded short self-introduction videos.
  • A separate group of 63 students rated the speakers on expressiveness, friendliness, and likability.
  • The AI tool OpenFace 2.0 was used to analyze micro-movements in facial muscles during the videos.
Key Findings

The study revealed that:

  • Students with subthreshold depressive symptoms were perceived as less friendly, expressive, and likable.
  • They did not appear more stiff or fake, indicating that StD may reduce positive expressivity rather than create overt negativity.
  • Specific patterns of eye and mouth movements, such as brow raises and lip stretches, were more common in participants with StD.
Cultural Considerations

The researchers emphasized the importance of cultural context, noting that the study focused on Japanese students, where emotional expression can vary significantly.

Implications for Mental Health

According to Sugimori, the approach of using self-introduction videos combined with automated facial expression analysis could be beneficial for:

  • Screening mental health in educational institutions and workplaces.
  • Monitoring psychological well-being through digital health platforms.
  • Implementing early interventions for mental health issues.

In conclusion, this study presents a non-invasive AI-based tool for the early detection of depression, potentially allowing for timely mental health care before clinical symptoms manifest.

Reference

Sugimori, E., & Yamaguchi, M. (2025). Subthreshold depression is associated with altered facial expression and impression formation via subjective ratings and action unit analysis. Sci Rep, 15(1), 30761. doi: 10.1038/s41598-025-15874-0

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