Early diagnosis and developmental outcome prediction of agenesis of the corpus callosum via an interpretable deep multimodal fusion model

Objective: Agenesis of the corpus callosum (ACC) presents with highly heterogeneous clinical features. Common methods rarely achieve accurate prenatal or early postnatal diagnosis and prognosis. We aimed to develop and test an interpretable deep neural network (DNN) that combines multimodal clinical data to improve diagnostic accuracy and neurodevelopmental outcome prediction. Methods: ) served as primary metrics. Results: < 0.001). Ablation experiments confirmed eight layers, sixteen neurons per layer, a learning rate of 0.01 and ten training epochs as the optimal configuration. Additional layers or higher learning rates caused overfitting. Conclusion: The proposed interpretable DNN framework outperforms traditional classifiers in early ACC diagnosis and developmental outcome prediction. It provides a potential tool for clinical decision support. Larger samples and integration of raw imaging data are needed to enhance prediction of complex phenotypes such as cerebral palsy and epilepsy.