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1		-== **1. Data Management & Integration** ==
2		-
3		-* Upload and organize harmonized biomarker datasets (PROMINENT).
4		-* Store EEG, sleep, and neuroimaging data in the EBRAINS Bucket.
5		-* Convert all datasets to .csv, .json, or .h5 formats for easier AI processing.
6		-* Implement automated data ingestion scripts to streamline updates from new sources.
7		-* Set up data harmonization methods to ensure consistency across different sources.
8		-* Enable Federated Learning to train AI models on multi-center data without sharing raw patient data (GDPR-compliant).
9		-
10		-== **2. AI-Based Risk Prediction & Diagnosis** ==
11		-
12		-* Implement machine learning models for dementia risk stratification (LETHE).
13		-* Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
14		-* Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
15		-* Store pretrained models in the /models/ directory for future use.
16		-* Implement real-time AI-based diagnostic annotation for clinicians.
17		-* Add confidence intervals to AI predictions for clinical decision support.
18		-* Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
19		-* Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
20		-* Explore the use of Large Language Models (LLMs) for medical report summarization.
21		-
22		-== **3. EEG, Neuroimaging & Sleep Analysis** ==
23		-
24		-* Process EEG/MEG data using MNE-Python (AI-Mind).
25		-* Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
26		-* Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
27		-* Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
28		-* Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.
29		-
30		-== **4. Clinical Validation & Pilot Testing** ==
31		-
32		-* Design a pilot study to validate AI-generated diagnostic scores.
33		-* Recruit a multicenter clinical validation cohort across European research hospitals.
34		-* Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
35		-* Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
36		-* Conduct a prospective study to test predictive accuracy over time.
37		-* Implement clinician feedback loops to refine the AI model based on real-world usage.
38		-* Publish validation results in peer-reviewed journals for credibility.
39		-
40		-== **5. Ethical, Regulatory & GDPR Compliance** ==
41		-
42		-* Ensure AI models comply with the EU AI Act for medical applications.
43		-* Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
44		-* Develop patient data anonymization pipelines before AI processing.
45		-* Secure ethics approval for data usage in clinical applications.
46		-* Set up consent management systems for patient data contributions.
47		-* Ensure interoperability with hospital Electronic Health Records (EHRs).
48		-
49		-== **6. EBRAINS Deployment & Cloud Infrastructure** ==
50		-
51		-* Deploy AI models on EBRAINS Cloud for real-time inference.
52		-* Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
53		-* Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
54		-* Optimize computational efficiency to ensure AI inference runs on real-time clinical data.
55		-
56		-== **7. Interactive Web App for Clinicians & Researchers** ==
57		-
58		-* Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
59		-* Allow clinicians to input biomarker data and get real-time AI predictions.
60		-* Enable PDF report generation for clinical decision-making.
61		-* Integrate custom dashboards with risk stratification results.
62		-* Deploy the tool on **neurodiagnoses.com** using Netlify, Vercel, or AWS.
63		-
64		-== **8. Cross-Project Collaborations** ==
65		-
66		-* Partner with AI-Mind for integrating EEG-based predictive models.
67		-* Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
68		-* Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
69		-* Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
70		-* Expand partnerships with clinical institutions to increase dataset size.
71		-
72		-== **9. Long-Term Expansion & Future Goals** ==
73		-
74		-* Explore AI-powered disease progression models for tracking neurodegeneration over time.
75		-* Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
76		-* Investigate genomics and proteomics for precision diagnostics.
77		-* Integrate wearable health tracking for continuous cognitive assessment.
78		-* Create an open-access AI diagnostic API for global research collaborations.
	1	+== dd ==