Wiki source code of to-do-list

Last modified by manuelmenendez on 2025/02/08 17:21

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3.1	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.
1.3	2
3.1	3	----
1.3	4
3.1	5	== 1. Data Management & Integration ==
1.3	6
3.1	7	* (((
	8	Data Acquisition & Storage:
1.3	9
3.1	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:
1.3	15
3.1	16	* Convert all datasets to standardized formats (.csv, .json, .h5) to facilitate AI processing.
	17	)))
	18	* (((
	19	Data Harmonization:
1.3	20
3.1	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:
1.3	26
3.1	27	* Enable federated learning techniques to train AI models on multi-center data without sharing raw patient data (ensuring GDPR compliance).
	28	)))
1.3	29
3.1	30	----
1.3	31
3.1	32	== 2. AI-Based Risk Prediction & Diagnosis ==
1.3	33
3.1	34	* (((
	35	Predictive Modeling:
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3.1	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:
1.3	42
3.1	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:
1.3	48
3.1	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	)))
1.3	56
3.1	57	----
1.3	58
3.1	59	== 3. EEG, Neuroimaging & Sleep Analysis ==
1.3	60
3.1	61	* (((
	62	EEG/MEG Analysis:
1.3	63
3.1	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:
2.1	69
3.1	70	* Integrate sleep data from wearables (smartwatches, headbands) as early biomarkers.
	71	)))
	72	* (((
	73	Neuroimaging Analysis:
2.1	74
3.1	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	)))
2.1	78
3.1	79	----
2.1	80
3.1	81	== 4. Clinical Validation & Pilot Testing ==
2.1	82
3.1	83	* (((
	84	Pilot Study Design:
2.1	85
3.1	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
2.1	98	* Implement clinician feedback loops to refine the AI model based on real-world usage.
3.1	99	* Publish validation results in peer-reviewed journals to enhance credibility.
	100	)))
2.1	101
3.1	102	----
2.1	103
3.1	104	== 5. Ethical, Regulatory & GDPR Compliance ==
2.1	105
3.1	106	* (((
	107	Regulatory Compliance:
2.1	108
3.1	109	* Ensure all AI models comply with relevant regulations (e.g., EU AI Act, GDPR).
	110	)))
	111	* (((
	112	Privacy Preservation:
2.1	113
3.1	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:
2.1	119
3.1	120	* Establish consent management systems for patient data contributions.
	121	* Ensure interoperability with hospital Electronic Health Record (EHR) systems.
	122	)))
2.1	123
3.1	124	----
2.1	125
3.1	126	== 6. EBRAINS Deployment & Cloud Infrastructure ==
2.1	127
3.1	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.
2.1	135
3.1	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
4.1	188	* GitHub Repository: [[Neurodiagnoses on GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
3.1	189	* EBRAINS Collaboratory: [[Neurodiagnoses on EBRAINS>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]

Wiki source code of to-do-list

Neurodiagnoses