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SUPPLEMENTAL MATERIALS IN PDF FILE BELOW
- Mazur_Supplemental_Materials.pdf -
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- Mazur_Supplemental_Materials.pdf -
VISUAL ABSTRACT IN PDF FILE BELOW
- Mazur_Visual_Abstract.pdf -
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- Mazur_Visual_Abstract.pdf -
PLAIN-LANGUAGE SUMMARY
Original Research
AI-Based Voice Biomarker Tool Shows Promise in Detecting Moderate to Severe Depression
Background and Goal:Depression is a leading cause of disability, impacting an estimated 18 million Americans each year, with a lifetime prevalence of major depression approaching 30%. Despite recommendations for universal screening, depression screening rarely occurs in the outpatient setting with some estimates placing screening rates at less than 4% of primary care encounters. This 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.
Study Approach: 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).Main Results: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.
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The tool demonstrated a sensitivity of 71%, meaning it correctly identified depression in 71% of people who had it.
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Specificity was 74%, indicating that the tool correctly ruled out depression in 74% of people who did not have it.
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In about 20% of cases, the tool flagged results as uncertain, recommending further evaluation by a clinician.
Why It Matters: While not a replacement for formal clinical interviews or assessments by qualified clinicians, the study findings suggest that machine learning technology could serve as a complementary decision-support tool. These findings are preliminary, and more work is needed to validate the tool and explore its integration into primary care workflows. This study represents a promising avenue for using physiologic voice biomarkers to assist clinicians in identifying and addressing depression, with future research needed to refine the technology and assess its broader applicability.
Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression
Alexa Mazur, BA, et al
Kintsugi Mindful Wellness, Inc, San Francisco, California
Visual Abstract:
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