Machine learning could be a complementary decision support tool for depression assessment

Machine learning could be a complementary decision support tool for depression assessment

Background and Purpose: Depression affects an estimated 18 million Americans each year, yet depression screening occurs rarely in the outpatient setting. This study evaluated an AI-based machine biomarker tool that uses speech patterns to detect moderate to severe depression to improve access to screening in primary care.

Study Approach: The study analyzed over 14,000 speech samples from U.S. and Canadian adults. Participants answered the question: “How was your day?” With at least 25 seconds of free-form language. The tool analyzed vocal biomarkers associated with depression, including speech cadence, hesitations, pauses and other acoustic features. These were compared with the results of 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, signs of depression that were not detected, and further assessment (for uncertain cases).

Key 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 accuracy.

• The tool showed a sensitivity of 71% and correctly identified depression in 71% of people who had it.

• The specificity was 74%, which correctly ruled out depression in 74% of people who didn't have it.

Why it matters: The study's findings suggest that machine learning technology could serve as a complementary decision support tool for depression assessment.

Sources: