A twin-aware multimodal deep learning framework with optimized late fusion for early prediction of adolescent anxiety disorder

Mental health related problems in adolescents are not always properly evaluated because of incomplete evaluation methods that do not combine biological, behavioral, and demographic details. Therefore, our study proposes a twin-aware multimodal deep learning framework applied to the QTAB dataset for early prediction of adolescent anxiety disorders. We employ a 3D convolutional neural network for neuroimaging data and prototype-based learning modules with residual encoders for behavioral and phenotypic data. Each modality-specific encoder learns compact representations optimized for class-imbalanced prediction through multi-loss objective functions. Calibrated probability outputs from the three modules are combined via optimized weighted late fusion. The framework achieves an AUC of 0.8935 (95% CI: 0.792-0.969), representing an absolute gain of 11 percentage points over the best unimodal baseline (questionnaire: AUC = 0.7766), with a sensitivity of 85.7% and a specificity of 87.3%. Pairwise statistical testing indicated that the classification patterns of the fusion model differ significantly from the questionnaire-only baseline (McNemar p = 0.0008), though AUC differences did not reach statistical significance at this sample size (DeLong p > 0.05). The best fusion weights were 23% MRI, 63% questionnaire, and 14% phenotypic, highlighting the dominant role of behavioral data. These results demonstrate that calibrated late fusion of multimodal predictions provides robust performance for early adolescent anxiety screening in twin cohorts with family-aware evaluation protocols.