Early Detection and AI-Driven Intervention Models for Pediatric Mental Health Disorders: A Multi-Center Longitudinal Study

Pediatric mental health challenges have emerged as a global concern, with anxiety, depression, and attention-deficit disorders showing increasing prevalence among children and adolescents post-pandemic. Despite advances in clinical diagnostics, early detection remains difficult due to subtle symptom onset and psychosocial variability. This study introduces an AI-assisted screening and intervention framework designed to identify early behavioral and emotional dysregulation in children aged 6–14 years.

A multi-center cohort (n = 1,200) across five countries was monitored over 18 months using digital behavioral tracking, clinician-verified psychological scales, and neurocognitive task performance. Machine learning models (random forest and LSTM classifiers) analyzed multimodal data, including speech prosody, facial expression metrics, and attention span variability. Model accuracy was validated against clinical diagnoses through receiver operating characteristic (ROC) analysis.

Results indicated that the AI model achieved AUC = 0.92 for early depressive traits and AUC = 0.89 for attentional dysregulation. Behavioral feedback-based digital interventions improved emotional regulation scores by 27% over standard care. Moreover, integration with telepsychiatry platforms enabled real-time support for families and clinicians.

These findings support a transformative paradigm in pediatric psychiatry, where AI-augmented systems enable scalable, individualized, and preventive mental health care. By merging data-driven analytics with clinical insight, this approach could significantly reduce diagnostic delays and enhance long-term psychosocial outcomes for children worldwide.