Objective:To develop an imaging-assisted diagnostic tool for schizophrenia based on multimodal magnetic resonance imaging and artificial intelligence techniques.Methods:Three independent datasets were utilized. For each subject, four brain structural metrics including grey matter volume (GMV), white matter volume (WMV), cortical thickness (CT) and deformation-based morphometry (DBM) indicators were extracted from the structural magnetic resonance imaging (sMRI) data, and three brain functional metrics including amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were extracted from the functional magnetic resonance imaging (fMRI) data. To distinguish patients with schizophrenia and healthy controls, single-metric classification models and multi-metrics-fusion classification models were trained and tested using a within-dataset and a between-dataset cross-validation strategy.Results:The results of within-dataset cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 86.18% (FC), while the multi-metric-fusion classifications could reach an accuracy of 90.21%. The results of between-datasets cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 69.02% (ReHo), while the multi-metric-fusion classifications could reach an accuracy of 71.25%.Conclusion:The functional metrics generally outperforms the structural metrics for the classification between patients with schizophrenia and heathy controls. Additionally, fusion of multi-modal brain imaging metrics can improve the classification performance. Specifically, the fusion of CT, DBM, WMV, FC and ReHo demonstrates the highest classification accuracy, which is a potential tool for imaging-assisted diagnosis of schizophrenia.