INTEGRATING FACIAL PROFILING AND STRUCTURED CLINICAL INTERVIEWS FOR PSYCHOLOGICAL ASSESSMENT IN DENTAL STUDENTS: A MIXED-METHODS STUDY

Abstract

 This study explores the convergence of facial profiling, structured clinical interviews (SCID), and validated psychometric tools in detecting burnout and depression among dental students. The aim is to establish a comprehensive and scalable model for psychological distress screening. A mixed-methods, cross-sectional study was conducted on 100 dental students. Standardized tools included the PHQ-9 and MBI-SS. Facial images were analyzed using OpenFace 2.0 for activation patterns such as AU4 and AU12. A subset of 20 participants underwent SCID interviews to cross-validate self-report and facial metrics. Facial profiling revealed a significant correlation between AU4 intensity and burnout severity (r = 0.44, p < .01). Depression scores showed a predictive relationship with mid-facial asymmetry and reduced AU12 expression. SCID assessments confirmed distress patterns identified via facial analytics. The combined model predicted 43% of variance in psychological distress scores (adjusted R² = .43). The fusion of facial analytics and SCID provides a robust, bias-mitigated framework for identifying mental health risks in dental education. This study introduces an interdisciplinary model with potential utility in academic and clinical settings.

Keywords: facial profiling, SCID, burnout, depression, dental education, affective computing