AD-Stage-Net v2: Multimodal Alzheimer’s Disease Prediction
AD-Stage-Net v2 combines structural MRI with clinical and cognitive information to evaluate future Alzheimer’s disease outcomes.
Original Staging Models: https://huggingface.co/spaces/katelynhur/AD-Stage-Net
Upload a whole-volume T1 MRI (.nii / .nii.gz). The application automatically
performs anatomical segmentation and ROI extraction before multimodal inference.
Prediction Tasks
- Progression — predicts future disease progression
- Development — predicts future development of Alzheimer’s-related impairment
MRI Strategies
- Multi-Region ROI: hippocampus + entorhinal cortex + amygdala + ventricles
- Hippocampal ROI: focused hippocampal representation
Research demonstration only — not a medical device or clinical diagnostic tool.
Run a Prediction
Task: predict future disease progression
Input: whole-volume T1 MRI (.nii / .nii.gz)
Automatic ROIs: hippocampus + entorhinal cortex + amygdala + ventricles
Clinical/cognitive data: used in multimodal fusion
1. Upload MRI
Supported: NIfTI whole-volume MRI (.nii, .nii.gz).
2. Clinical / Cognitive Information
Enter the measurements available for the participant. Leave unavailable values blank.
Clinical / cognitive features
total_gray_matter_volume |
3. Run Model
How the pipeline works
Whole-volume T1 MRI
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FastSurfer segmentation
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Automatic ROI extraction
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MRI embedding
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Clinical + cognitive fusion
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Prediction
Prediction Tasks
Progression
Predicts future disease progression using information available at the current visit.
Development
Predicts whether the participant will develop future Alzheimer’s-related impairment.
MRI Strategies
Multi-Region ROI
Hippocampus, entorhinal cortex, amygdala, and ventricles.
Hippocampal ROI
Hippocampus only.