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Background <p>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).</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Xingyan Le,
  • Mingguang Yang,
  • Chang Li,
  • Qingbiao Zhang,
  • Yuyin Wang,
  • Xiaoli Yu,
  • Yuwei Xia,
  • Feng Shi,
  • Junbang Feng,
  • Chuanming Li

摘要

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.