Exploring Burnout and Depression in Dental Graduates Using Facial Profiling 1 co
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Date
2025-06-07
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Abstract
Abstract
Purpose: This study examines the efficacy of facial action unit
(AU) analysis as an objective biomarker for detecting burnout
and depression among dental graduates, addressing limitations
of self-report measures in high-stress academic environments.
Materials and Methods: A cross-sectional design was
employed with 100 dental students (58% female; mean
age=21.3±1.9 years). Participants completed the PHQ-9 for
depression and MBI-Student Survey for burnout, while
OpenFace 2.0 analyzed neutral facial images for AU4 (brow
lowerer), AU1 (inner brow raiser), and facial asymmetry
indices. Multiple regression analyses controlled for age and
gender.
Key Results: Strong correlation between AU4 intensity and
burnout scores (r=0.42, p<0.01).Sadness Index predicted 38% of
depression variance (R²=0.38, β=0.42, p<0.001).Combined
facial metrics outperformed single predictors (ΔR²=0.12,
p<0.01)
Conclusions: Automated facial analysis demonstrates clinical
potential as a supplementary screening tool, with AU4 and facial
asymmetry serving as robust physiological markers of
psychological distress in dental education settings.
Keywords: affective computing; mental health screening; action
units; academic stress; machine learning