A machine learning tool successfully identified vocal markers of depression in over 70% of cases within 25 seconds, highlighting its potential for improving mental health screening in primary care and virtual healthcare settings. Study: Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression . Image Credit: PeopleImages.
com - Yuri A/Shutterstock.com In a recent article in The Annals of Family Medicine , researchers evaluated the effectiveness of a machine learning (ML) tool for detecting vocal signs linked to severe or moderate depression. The tool successfully detected vocal markers of depression in just 25 seconds, correctly identifying cases of depression in more than 70% of samples, highlighting its utility for mental health screening.
Background Depression is a major health issue, affecting about 18 million Americans annually, with nearly 30% experiencing it at some point in their lives. Despite guidelines recommending universal screening, depression screening in primary care remains very low (<4%), and even when screening is recommended, fewer than 50% of eligible patients are tested. ML has the potential to improve screening rates without adding extra administrative work.
People experiencing depression often have distinct speech patterns, including stuttering, hesitations, longer pauses, and slower speech. ML can analyze these vocal traits, known as voice biomarkers, to detect signs of depression. Using ML for voic.
