Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.

ABSTRACT:

Background

Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely intervention and improved outcomes. Multimodal speech analyses hold promise for capturing psychomotor, cognitive, and affective changes associated with bipolar depression.

Objective

This study aims to develop between- and within-person classifiers to screen for bipolar depression and monitor longitudinal changes to detect depressive recurrence in patients with bipolar disorder. A secondary objective was to compare the predictive performance across speech modalities.

Methods

We collected 304 voice audio recordings obtained durin

SUBMITTER: Min S 

PROVIDER: S-EPMC12715464 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature