Doctor-patient conversations in primary care revealed vocal cues associated with cognitive impairment.A model trained on acoustic features of these conversations identified impairment in patients with moderate sensitivity and specificity.Measures of pitch, timing, and speech variability were key predictors of cognitive impairment.
Short segments of conversations between primary care clinicians and patients contained signals that helped detect undiagnosed cognitive impairment, an acoustic analysis suggested.
A machine learning model trained on acoustic features from recordings of primary care visits achieved a sensitivity of 68.2%, specificity of 63.6%, and positive predictive value of 30.4% for identifying cognitive impairment, reported Joseph Colonel, PhD, of the Icahn School of Medicine at Mount Sinai in New York City, and co-authors.
The model had an area under the receiver operating characteristic curve (AUROC) of 0.733 and a maximum F1 score (Fmax) of 0.502, Colonel and colleagues wrote in JAMA Neurology.
AUROC and Fmax values were similar in a validation cohort. Measures of pitch, timing, and speech variability were key predictors of cognitive impairment.
