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1		-== xHeadings Will Appear in Table of Content ==
	1	+== **1. Data Management & Integration** ==
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	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).
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	10	+== **2. AI-Based Risk Prediction & Diagnosis** ==
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6		->This is a quote. You can add a quote by selecting some text and clicking the quote button in the editor.
	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.
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	22	+== **3. EEG, Neuroimaging & Sleep Analysis** ==
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	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.
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	30	+== **4. Clinical Validation & Pilot Testing** ==
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	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.
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	40	+== **5. Ethical, Regulatory & GDPR Compliance** ==
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	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).
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	49	+== **6. EBRAINS Deployment & Cloud Infrastructure** ==
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	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.
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	56	+== **7. Interactive Web App for Clinicians & Researchers** ==
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	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.