How Accurately Can AI Tool Detect Depression? Study Provides Insights
A new study evaluated an AI-based machine learning biomarker tool that uses speech patterns to detect moderate to severe depression, aiming to improve access to screening in primary care settings.
The study analyzed over 14,000 voice samples from U.S. and Canadian adults. Participants answered the question, “How was your day?” with at least 25 seconds of free-form speech. The tool analyzed vocal biomarkers associated with depression, including speech cadence, hesitations, pauses, and other acoustic features. These were compared to results from the Patient Health Questionnaire-9 (PHQ-9), a standard depression screening tool. A PHQ-9 score of 10 or higher indicated moderate to severe depression. The AI tool provided three outputs: Signs of Depression Detected, Signs of Depression Not Detected, and Further Evaluation Recommended (for uncertain cases).
The dataset used to train the AI model consisted of 10,442 samples, while an additional 4,456 samples were used in a validation set to assess its accuracy.
The tool demonstrated a sensitivity of 71%, correctly identifying depression in 71% of people who had it.
Specificity was 74%, correctly ruling out depression in 74% of people who did not have it.
The study findings suggest that machine learning technology could serve as a complementary decision-support tool for assessing depression.
Reference: Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression
Alexa Mazur, Harrison Costantino, Prentice Tom, Michael P. Wilson, Ronald G. Thompson
The Annals of Family Medicine Jan 2025, 23 (1) 60-65; DOI: 10.1370/afm.240091
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