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1		-== **1. Data Management & Integration** ==
	1	+This document outlines the full workflow for Neurodiagnoses—from data acquisition and AI model training to clinical validation, ethical compliance, cloud deployment, and future expansion into CNS Digital Twins.
2	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).
	3	+----
9	9
10		-== **2. AI-Based Risk Prediction & Diagnosis** ==
	5	+== 1. Data Management & Integration ==
11	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.
	7	+* (((
	8	+**Data Acquisition & Storage:**
21	21
22		-== **3. EEG, Neuroimaging & Sleep Analysis** ==
	10	+* Download raw data from external sources (e.g., ADNI, GP2, PPMI, Enroll-HD, UK Biobank, etc.).
	11	+* Upload and organize datasets in EBRAINS Buckets and in the /datasets/ directory on GitHub.
	12	+)))
	13	+* (((
	14	+**Data Conversion & Format:**
23	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.
	16	+* Convert all datasets to standardized formats (.csv, .json, .h5) to facilitate AI processing.
	17	+)))
	18	+* (((
	19	+**Data Harmonization:**
29	29
30		-== **4. Clinical Validation & Pilot Testing** ==
	21	+* Implement automated data ingestion scripts to streamline updates from new sources.
	22	+* Set up data harmonization methods to ensure consistency across different sources (e.g., genetics, neuroimaging, biomarkers, digital health).
	23	+)))
	24	+* (((
	25	+**Federated Learning:**
31	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.
	27	+* Enable federated learning techniques to train AI models on multi-center data without sharing raw patient data (ensuring GDPR compliance).
	28	+)))
39	39
40		-== **5. Ethical, Regulatory & GDPR Compliance** ==
	30	+----
41	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).
	32	+== 2. AI-Based Risk Prediction & Diagnosis ==
48	48
49		-== **6. EBRAINS Deployment & Cloud Infrastructure** ==
	34	+* (((
	35	+**Predictive Modeling:**
50	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.
	37	+* Implement machine learning models (e.g., Random Forest, Neural Networks) for dementia risk stratification.
	38	+* Develop probabilistic models (e.g., KNN Imputer, Bayesian approaches) to handle missing data.
	39	+)))
	40	+* (((
	41	+**Training with Multi-Modal Data:**
55	55
56		-== **7. Interactive Web App for Clinicians & Researchers** ==
	43	+* Train AI models using data from biomarkers, EEG, MRI, and lifestyle factors.
	44	+* Store pre-trained models in the /models/ directory for future use.
	45	+)))
	46	+* (((
	47	+**Diagnostic Annotation System:**
57	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.
	49	+* Implement real-time AI-based diagnostic annotation that produces two types of reports for each case:
	50	+** **Probabilistic Diagnosis:** Traditional diagnosis with associated probability percentages.
	51	+** **Tridimensional Diagnosis:** A structured classification based on three axes—etiology, molecular markers, and neuroanatomoclinical correlations.
	52	+* Integrate Explainable AI techniques (e.g., SHAP) to ensure transparency in predictions.
	53	+* Explore advanced deep learning methods for pattern recognition in neuroimaging data.
	54	+* Investigate the use of Large Language Models (LLMs) for summarizing and generating medical reports.
	55	+)))
63	63
64		-== **8. Cross-Project Collaborations** ==
	57	+----
65	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.
	59	+== 3. EEG, Neuroimaging & Sleep Analysis ==
71	71
72		-== **9. Long-Term Expansion & Future Goals** ==
	61	+* (((
	62	+**EEG/MEG Analysis:**
73	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.
	64	+* Process EEG/MEG data using tools like MNE-Python.
	65	+* Apply spectral analysis and connectivity metrics to derive EEG biomarkers for dementia detection.
	66	+)))
	67	+* (((
	68	+**Sleep Monitoring:**
79	79
80		-== **2. AI-Based Risk Prediction & Diagnosis** ==
	70	+* Integrate sleep data from wearables (smartwatches, headbands) as early biomarkers.
	71	+)))
	72	+* (((
	73	+**Neuroimaging Analysis:**
81	81
82		-* Implement machine learning models for dementia risk stratification (LETHE).
83		-* Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
84		-* Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
85		-* Store pretrained models in the /models/ directory for future use.
86		-* Implement real-time AI-based diagnostic annotation for clinicians.
87		-* Add confidence intervals to AI predictions for clinical decision support.
88		-* Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
89		-* Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
90		-* Explore the use of Large Language Models (LLMs) for medical report summarization.
	75	+* Utilize MRI volumetric analysis to assess brain atrophy in high-risk patients.
	76	+* Implement functional MRI (fMRI) analysis to correlate neuroanatomical changes with cognitive function.
	77	+)))
91	91
92		-== **3. EEG, Neuroimaging & Sleep Analysis** ==
	79	+----
93	93
94		-* Process EEG/MEG data using MNE-Python (AI-Mind).
95		-* Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
96		-* Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
97		-* Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
98		-* Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.
	81	+== 4. Clinical Validation & Pilot Testing ==
99	99
100		-== **4. Clinical Validation & Pilot Testing** ==
	83	+* (((
	84	+**Pilot Study Design:**
101	101
102		-* Design a pilot study to validate AI-generated diagnostic scores.
103		-* Recruit a multicenter clinical validation cohort across European research hospitals.
104		-* Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
105		-* Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
106		-* Conduct a prospective study to test predictive accuracy over time.
	86	+* Design a multicenter pilot study to validate AI-generated diagnostic scores.
	87	+* Recruit a clinical validation cohort from European research hospitals.
	88	+)))
	89	+* (((
	90	+**Performance Evaluation:**
	91	+
	92	+* Compare AI-based diagnoses with traditional clinician diagnoses.
	93	+* Develop and track validation metrics (e.g., AUROC, precision-recall, false positive rates).
	94	+)))
	95	+* (((
	96	+**Feedback and Refinement:**
	97	+
107	107	* Implement clinician feedback loops to refine the AI model based on real-world usage.
108		-* Publish validation results in peer-reviewed journals for credibility.
	99	+* Publish validation results in peer-reviewed journals to enhance credibility.
	100	+)))
109	109
110		-== **5. Ethical, Regulatory & GDPR Compliance** ==
	102	+----
111	111
112		-* Ensure AI models comply with the EU AI Act for medical applications.
113		-* Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
114		-* Develop patient data anonymization pipelines before AI processing.
115		-* Secure ethics approval for data usage in clinical applications.
116		-* Set up consent management systems for patient data contributions.
117		-* Ensure interoperability with hospital Electronic Health Records (EHRs).
	104	+== 5. Ethical, Regulatory & GDPR Compliance ==
118	118
119		-== **6. EBRAINS Deployment & Cloud Infrastructure** ==
	106	+* (((
	107	+**Regulatory Compliance:**
120	120
121		-* Deploy AI models on EBRAINS Cloud for real-time inference.
122		-* Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
123		-* Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
124		-* Optimize computational efficiency to ensure AI inference runs on real-time clinical data.
	109	+* Ensure all AI models comply with relevant regulations (e.g., EU AI Act, GDPR).
	110	+)))
	111	+* (((
	112	+**Privacy Preservation:**
125	125
126		-== **7. Interactive Web App for Clinicians & Researchers** ==
	114	+* Implement privacy-preserving techniques (Federated Learning, Differential Privacy) to protect patient data.
	115	+* Develop data anonymization pipelines prior to AI processing.
	116	+)))
	117	+* (((
	118	+**Consent & Data Governance:**
127	127
128		-* Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
129		-* Allow clinicians to input biomarker data and get real-time AI predictions.
130		-* Enable PDF report generation for clinical decision-making.
131		-* Integrate custom dashboards with risk stratification results.
132		-* Deploy the tool on **neurodiagnoses.com** using Netlify, Vercel, or AWS.
	120	+* Establish consent management systems for patient data contributions.
	121	+* Ensure interoperability with hospital Electronic Health Record (EHR) systems.
	122	+)))
133	133
134		-== **8. Cross-Project Collaborations** ==
	124	+----
135	135
136		-* Partner with AI-Mind for integrating EEG-based predictive models.
137		-* Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
138		-* Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
139		-* Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
140		-* Expand partnerships with clinical institutions to increase dataset size.
	126	+== 6. EBRAINS Deployment & Cloud Infrastructure ==
141	141
142		-== **9. Long-Term Expansion & Future Goals** ==
	128	+* **Cloud Deployment:**
	129	+** Deploy AI models on the EBRAINS Cloud for real-time inference.
	130	+* **Collaborative Development:**
	131	+** Set up Jupyter Notebooks in EBRAINS Lab for collaborative development and testing.
	132	+** Automate model training pipelines using GitHub Actions or EBRAINS HPC resources.
	133	+* **Optimization:**
	134	+** Optimize computational efficiency to enable real-time processing of clinical data.
143	143
144		-* Explore AI-powered disease progression models for tracking neurodegeneration over time.
145		-* Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
146		-* Investigate genomics and proteomics for precision diagnostics.
147		-* Integrate wearable health tracking for continuous cognitive assessment.
148		-* Create an open-access AI diagnostic API for global research collaborations.
	136	+----
	137	+
	138	+== 7. Interactive Web Application for Clinicians & Researchers ==
	139	+
	140	+* **Web App Development:**
	141	+** Develop an interactive web-based diagnostic tool using frameworks such as Flask, FastAPI, or Streamlit.
	142	+** Allow clinicians to input biomarker data and receive real-time AI predictions.
	143	+* **Report Generation:**
	144	+** Enable the generation of PDF reports for clinical decision support.
	145	+* **Custom Dashboards:**
	146	+** Integrate dashboards that display risk stratification results.
	147	+* **Deployment:**
	148	+** Deploy the web app on neurodiagnoses.com using hosting services like Netlify, Vercel, or AWS.
	149	+
	150	+----
	151	+
	152	+== 8. Cross-Project Collaborations ==
	153	+
	154	+* (((
	155	+**External Partnerships:**
	156	+
	157	+* Collaborate with projects such as AI-Mind for EEG-based predictive modeling.
	158	+* Work with LETHE for lifestyle-based cognitive decline risk scoring.
	159	+* Leverage PROMINENT’s multi-modal AI pipeline to refine dementia subtype classification.
	160	+* Expand partnerships with clinical institutions to enhance dataset diversity.
	161	+)))
	162	+* (((
	163	+**Open-Source Community:**
	164	+
	165	+* Encourage contributions via GitHub (code improvements, new features) and EBRAINS discussion pages (research and validation).
	166	+)))
	167	+
	168	+----
	169	+
	170	+== 9. Long-Term Expansion & Future Goals ==
	171	+
	172	+* **Disease Progression Modeling:**
	173	+** Explore AI-powered models for tracking neurodegeneration over time.
	174	+* **CNS Digital Twins:**
	175	+** Develop CNS Digital Twins by integrating multi-omics data, neuroimaging, and digital health records to create personalized simulations of disease progression.
	176	+* **Continuous Monitoring:**
	177	+** Investigate the integration of wearable health tracking devices for ongoing cognitive assessment.
	178	+* **Open-Access API:**
	179	+** Create an API to allow global research collaborations with access to AI diagnostic tools.
	180	+* **Sustainability & Updates:**
	181	+** Regularly update the system with new data and algorithm improvements.
	182	+** Establish long-term funding and partnership strategies to ensure sustainability.
	183	+
	184	+----
	185	+
	186	+=== Key Resources ===
	187	+
	188	+* **GitHub Repository:** [[Neurodiagnoses on GitHub>>url:https://github.com/manuelmenendezgonzalez/neurodiagnoses]]
	189	+* **EBRAINS Collaboratory:** [[Neurodiagnoses on EBRAINS>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]