1. Data Management & Integration

• Upload and organize harmonized biomarker datasets (PROMINENT).
• Store EEG, sleep, and neuroimaging data in the EBRAINS Bucket.
• Convert all datasets to .csv, .json, or .h5 formats for easier AI processing.
• Implement automated data ingestion scripts to streamline updates from new sources.
• Set up data harmonization methods to ensure consistency across different sources.
• Enable Federated Learning to train AI models on multi-center data without sharing raw patient data (GDPR-compliant).

2. AI-Based Risk Prediction & Diagnosis

• Implement machine learning models for dementia risk stratification (LETHE).
• Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
• Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
• Store pretrained models in the /models/ directory for future use.
• Implement real-time AI-based diagnostic annotation for clinicians.
• Add confidence intervals to AI predictions for clinical decision support.
• Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
• Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
• Explore the use of Large Language Models (LLMs) for medical report summarization.

3. EEG, Neuroimaging & Sleep Analysis

• Process EEG/MEG data using MNE-Python (AI-Mind).
• Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
• Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
• Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
• Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.

4. Clinical Validation & Pilot Testing

• Design a pilot study to validate AI-generated diagnostic scores.
• Recruit a multicenter clinical validation cohort across European research hospitals.
• Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
• Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
• Conduct a prospective study to test predictive accuracy over time.
• Implement clinician feedback loops to refine the AI model based on real-world usage.
• Publish validation results in peer-reviewed journals for credibility.

5. Ethical, Regulatory & GDPR Compliance

• Ensure AI models comply with the EU AI Act for medical applications.
• Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
• Develop patient data anonymization pipelines before AI processing.
• Secure ethics approval for data usage in clinical applications.
• Set up consent management systems for patient data contributions.
• Ensure interoperability with hospital Electronic Health Records (EHRs).

6. EBRAINS Deployment & Cloud Infrastructure

• Deploy AI models on EBRAINS Cloud for real-time inference.
• Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
• Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
• Optimize computational efficiency to ensure AI inference runs on real-time clinical data.

7. Interactive Web App for Clinicians & Researchers

• Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
• Allow clinicians to input biomarker data and get real-time AI predictions.
• Enable PDF report generation for clinical decision-making.
• Integrate custom dashboards with risk stratification results.
• Deploy the tool on neurodiagnoses.com using Netlify, Vercel, or AWS.

8. Cross-Project Collaborations

• Partner with AI-Mind for integrating EEG-based predictive models.
• Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
• Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
• Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
• Expand partnerships with clinical institutions to increase dataset size.

9. Long-Term Expansion & Future Goals

• Explore AI-powered disease progression models for tracking neurodegeneration over time.
• Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
• Investigate genomics and proteomics for precision diagnostics.
• Integrate wearable health tracking for continuous cognitive assessment.
• Create an open-access AI diagnostic API for global research collaborations.

2. AI-Based Risk Prediction & Diagnosis

• Implement machine learning models for dementia risk stratification (LETHE).
• Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
• Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
• Store pretrained models in the /models/ directory for future use.
• Implement real-time AI-based diagnostic annotation for clinicians.
• Add confidence intervals to AI predictions for clinical decision support.
• Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
• Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
• Explore the use of Large Language Models (LLMs) for medical report summarization.

3. EEG, Neuroimaging & Sleep Analysis

• Process EEG/MEG data using MNE-Python (AI-Mind).
• Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
• Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
• Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
• Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.

4. Clinical Validation & Pilot Testing

• Design a pilot study to validate AI-generated diagnostic scores.
• Recruit a multicenter clinical validation cohort across European research hospitals.
• Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
• Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
• Conduct a prospective study to test predictive accuracy over time.
• Implement clinician feedback loops to refine the AI model based on real-world usage.
• Publish validation results in peer-reviewed journals for credibility.

5. Ethical, Regulatory & GDPR Compliance

• Ensure AI models comply with the EU AI Act for medical applications.
• Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
• Develop patient data anonymization pipelines before AI processing.
• Secure ethics approval for data usage in clinical applications.
• Set up consent management systems for patient data contributions.
• Ensure interoperability with hospital Electronic Health Records (EHRs).

6. EBRAINS Deployment & Cloud Infrastructure

• Deploy AI models on EBRAINS Cloud for real-time inference.
• Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
• Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
• Optimize computational efficiency to ensure AI inference runs on real-time clinical data.

7. Interactive Web App for Clinicians & Researchers

• Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
• Allow clinicians to input biomarker data and get real-time AI predictions.
• Enable PDF report generation for clinical decision-making.
• Integrate custom dashboards with risk stratification results.
• Deploy the tool on neurodiagnoses.com using Netlify, Vercel, or AWS.

8. Cross-Project Collaborations

• Partner with AI-Mind for integrating EEG-based predictive models.
• Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
• Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
• Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
• Expand partnerships with clinical institutions to increase dataset size.

9. Long-Term Expansion & Future Goals

• Explore AI-powered disease progression models for tracking neurodegeneration over time.
• Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
• Investigate genomics and proteomics for precision diagnostics.
• Integrate wearable health tracking for continuous cognitive assessment.
• Create an open-access AI diagnostic API for global research collaborations.