AI Detects Early Signs of Depression Through Facial Expressions

Identifying early signs of depression is a significant challenge, as these indicators are often subtle and difficult to notice. In this context, a recent study has shown that artificial intelligence can detect these signs by analyzing the minute movements of facial muscles. This study was conducted on Japanese students experiencing subthreshold depression symptoms, revealing that they appeared less friendly and expressive to their peers, despite not appearing tense or artificial.

A New Study at Waseda University

At Waseda University, Assistant Professor Eriko Sugimori and researcher Mayo Yamaguchi conducted a study on the impact of subthreshold depression on facial expressions using artificial intelligence. The aim of this study was to explore how non-verbal cues, such as facial expressions, affect social impressions and mental health.

The study was published in the journal Scientific Reports on August 21, 2025, utilizing OpenFace 2.0 technology to analyze the subtle movements of facial muscles in short introductory videos of Japanese university students.

Study Results and AI Analysis

The study results showed that students with subthreshold depression symptoms were rated by their peers as less friendly and expressive. Interestingly, these students were not perceived as tense or artificial, indicating that subthreshold depression does not make individuals overtly negative but reduces their positive expressions.

AI analysis revealed specific patterns of eye and mouth movements, such as inner brow raises, upper eyelid raises, lip stretches, and mouth openings, which were more frequent among participants with subthreshold depression. These subtle movements were strongly associated with depression scores, even if they were too fine for untrained observers to notice.

Potential Applications and Future Studies

The study suggests that the innovative approach of using short video clips and automatic facial expression analysis can be applied to screen and detect mental health in schools, universities, and workplaces. This method could be used in mental health technologies, digital health platforms, or corporate wellness programs to efficiently monitor psychological well-being.

The findings of this study provide a new, accessible, and non-invasive tool based on facial expression analysis using AI for the early detection of depression before clinical symptoms appear, enabling early interventions and immediate mental health care.

Conclusion

In conclusion, this study highlights the significant potential of artificial intelligence in the mental health field, particularly in detecting early-stage depression through facial expression analysis. By understanding and utilizing this technology, we can improve screening methods and early interventions, contributing to enhanced mental health and overall well-being in various environments. This study represents an important step towards adopting innovative technological solutions in mental health.