Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.

Journal: Annals of family medicine
PMID: 39805690

Abstract

PURPOSE: Mental 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.

Authors

  • Alexa Mazur
    Kintsugi Mindful Wellness, Inc, San Francisco, California aam2213@columbia.edu.
  • Harrison Costantino
    Department of Computer Science, University of California, Berkeley, California.
  • Prentice Tom
    Kintsugi Mindful Wellness, Inc, San Francisco, California.
  • Michael P Wilson
    Departments of Psychiatry and Emergency Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
  • Ronald G Thompson
    Departments of Psychiatry and Emergency Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.

Keywords

External Resources

Popular Topics
Recent Journals