Salutogenic Approach to Early Kidney Health Promotion: Comparing Deep Learning Facial Analysis and Questionnaire Screening in Adolescents - Nurjannah, Uun; erlena, Erlena; wahyudi; Suryana, Iham (Universitas Airlangga, 2026)
Background: Chronic kidney disease (CKD) is increasingly reported in adolescents and is driven by modifiable lifestyle factors such as inadequate hydration, frequent consumption of processed foods, obesity, and poor sleep quality. Because early CKD is typically asymptomatic, routine laboratory screening is often impractical in school or community settings. Self-reported questionnaires can help estimate lifestyle-related risk but rely on subjective recall. Objective: To compare the effectiveness of a DL-based facial analysis model with self-reported questionnaire screening for early CKD risk in adolescents. Methods: A cross-sectional study was conducted among 100 adolescents aged 16–19 in Karawang, Indonesia. Participants completed validated questionnaires assessing hydration, dietary habits, and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Standardized facial photographs were analyzed using a convolutional neural network (CNN) trained to detect CKD-related facial markers. Agreement between methods was assessed using Cohen’s kappa, and diagnostic performance was evaluated using ROC analysis. Results: Questionnaire screening classified 88% of adolescents as low-risk, 10% moderate-risk, and 2% high-risk, while the AI model classified 95%, 4%, and 1%, respectively, demonstrating moderate agreement (κ = 0.61, p < 0.001). The AI model achieved 91.0% accuracy, 88.0% sensitivity, 92.0% specificity, and an AUC of 0.904. Overweight adolescents had higher odds of being at risk (OR = 2.35). Conclusion: Combining AI-based facial analysis with questionnaire assessment provides a rapid, scalable, and non-invasive strategy for early CKD risk screening in adolescents, particularly in resource-limited settings.