부산대학교 도서관

BackgroundMultiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. ObjectiveWe aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. MethodsWe utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. ResultsSuicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=−0.68, P=.02, r2=0.40), overall expressivity (β=−0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (β=−1.24, P=.006, r2=0.48) and head yaw variability (β=−0.54, P=.06, r2=0.32). ConclusionsDigital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation.