Screening with depression scales alone is subjective, and relying on single-modal data often leads to incomplete identification of symptoms that are easily missed or misdiagnosed. In this study, we first aim to use artificial intelligence to construct a depression symptom recognition model, concatenate multimodal features such as facial expression, audio, text, and postural behavior, and deeply fuse them to construct a multimodal model.
Study Type
OBSERVATIONAL
Enrollment
2,000
Collect the facial expressions, audio, text and postural behavior data of the respondents using electronic devices.
Wuhan Mental Health Center
Wuhan, Hubei, China
Use the PHQ-9 (Patient Health Questionnaire - 9 ) to assess whether the respondents have depressive symptoms. The scale score is one point for each item.
If the PHQ-9 score is 5 or higher, it is determined as a positive sign of depressive symptoms.
Time frame: 2025.09.01-2026.09.01
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