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

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Abstract

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.

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