Harnessing Acoustic Analysis in Primary Care to Detect Cognitive Impairment: A Novel Screening Approach

Highlight

This diagnostic study evaluates the feasibility and accuracy of machine learning (ML) models analyzing acoustic features from brief patient-primary care physician conversations to identify cognitive impairment (CI). Using data from over 900 patients across two urban centers, the study demonstrates good predictive ability with AUROC approximately 0.73. Key acoustic indicators include pitch, timing, and variability, supporting a passive, scalable screening tool in routine clinical care.

Study Background