Integrating AI-driven technologies and facial-semantic features for depression detection: A cross-sectional study.

BACKGROUND: Depression is a major global health concern, still individuals with depressive tendencies remain undetected in outpatient settings due to the limitations of self-report, stigma, and reporting bias. Advances in artificial intelligence (AI) offer opportunities for objective, scalable, and unobtrusive methods of early detection. This study examined predictive accuracy of two AI-driven systems, the iSeeME facial-expression model and the Event-Driven Depression Tendency Warning System (EDDTW-V2) in identifying depressive symptoms among high-risk outpatients in Taiwan. METHODS: A cross-sectional study was conducted with 62 outpatients recruited from psychiatric and surgical-oncology clinics at a medical center in southern Taiwan. Participants completed standardized depression assessments, including the Hamilton Depression Rating Scale (HDRS), Beck Depression Inventory-II (BDI-II), and Patient Health Questionnaire-9 (PHQ-9). Facial-expression data were analyzed using iSeeME, while narrative transcripts were processed with EDDTW-V2. Predictive validity was assessed through accurate metrics, correlations with clinical scales, and k-means cluster analysis. RESULTS: Of the 62 participants, 39 were clinically depressed (HDRS ≥7). The iSeeME system achieved precision of 0.761, recall of 0.854, F1-score of 0.805, and accuracy of 0.770, showing stronger correlations with BDI-II (r = 0.442, p < 0.01) and PHQ-9 (r = 0.335, p < 0.01) than EDDTW-V2. Cluster analysis revealed a high-distress subgroup characterized by younger age, single status, fewer children, and predominance of psychiatric patients. CONCLUSION: Both AI systems showed potential as complementary tools to traditional assessments. iSeeME was more sensitive to affective-behavioral symptoms, while EDDTW-V2 captured cognitive-linguistic features. They support earlier detection, monitoring, and targeted interventions in outpatient settings.