ArticleAnnals of family medicine2025
Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.
Article in Annals of family medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- The epistemic economy of vocal biomarker startups: a qualitative interview study of data practices and governance.Frontiers in digital health · 2026Article
- Article
- Ecological Momentary Assessment and Voice-Informed Forecast and Detection for the Diagnosis of Major Depression.Advances in experimental medicine and biology · 2026Review
- Listening to the Mind: Integrating Vocal Biomarkers into Digital Health.Brain sciences · 2025Article
- AI listens for health conditions.Nature · 2025Article
- Generative artificial intelligence in depression research: A bibliometric analysis of WoSCC-Indexed literature.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.
methodsWe performed a cross-sectional study from February 1, 2021 to July 31, 2022 to examine ≥25 seconds of free-form speech content from English-speaking samples captured from 14,898 unique adults in the United States and Canada. Participants were recruited via social media, provided informed consent, and their voice biomarker results were compared with a self-reported Patient Health Questionnaire-9 (PHQ-9) at a cut-off score of 10 (moderate to severe depression).
resultsFrom as few as 25 seconds of free-form speech, machine learning technology was able to detect vocal characteristics consistent with an increased PHQ-9 ≥10, with a sensitivity of 71.3 (95% CI, 69.0-73.5) and a specificity of 73.5 (95% CI, 71.5-75.5).
conclusionsMachine learning has potential utility in helping clinicians screen patients for moderate to severe depression. Further research is needed to measure the effectiveness of machine learning vocal detection and analysis technology in clinical deployment.
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