Automatic identification of different stage of Alzheimer's disease using multimodal MRI and artificial intelligence.

BACKGROUND: This study aimed to use multimodal MRI and artificial intelligence to automatically identify cognitive normal (CN), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and alzheimer’s disease (AD). METHODS: 715 participants with different cognitive status (CN, SCD, MCI, AD) were enrolled from ADNI (for training/validation) and OASIS-3 (for external validation). All participants underwent structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). The sMRI of whole brain was segmented into 116 regions and the volumes of each region was obtained using 3D-VB-Net. 4528 radiomics features were extracted from hippocampus. Functional metrics (ALFF, fALFF, ReHo, FC) for each brain region were calculated using rs-fMRI data. Least absolute shrinkage and selection operator (LASSO) and K-best were used to reduce feature dimensionality. Machine learning was performed using bagging decision tree (BDT), logistic regression (LR), support-vector-machine (SVM) and random-forest (RF) classifiers. Evaluation metrics included the area under the curve (AUC), specificity, sensitivity and F1-score. RESULTS: A total of 4528 radiomic features, 116 volume features, and 464 functional features were extracted for each participant. After dimensionality reduction, the BDT model based on volume-function-radiomics features achieved the highest performance, with macro-average AUCs of 0.918 (95% CI: 0.890–0.944, specificity = 0.922, sensitivity = 0.769), 0.862 (95% CI: 0.784–0.935, specificity = 0.897, sensitivity = 0.692), and 0.809 (95% CI: 0.724–0.885, specificity = 0.877, sensitivity = 0.630) in the training, internal, and external validation sets, respectively. CONCLUSIONS: This study was the first to develop a high-accuracy four-class classification model for CN, SCD, MCI, and AD identification by integrating multimodal MRI, deep learning and machine learning.