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Wiki source code of to-do-list

Last modified by manuelmenendez on 2025/02/08 17:21
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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.
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5 == 1. Data Management & Integration ==
6
7 * (((
8 **Data Acquisition & Storage:**
9
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.
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13 * (((
14 **Data Conversion & Format:**
15
16 * Convert all datasets to standardized formats (.csv, .json, .h5) to facilitate AI processing.
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18 * (((
19 **Data Harmonization:**
20
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).
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24 * (((
25 **Federated Learning:**
26
27 * Enable federated learning techniques to train AI models on multi-center data without sharing raw patient data (ensuring GDPR compliance).
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29
30 ----
31
32 == 2. AI-Based Risk Prediction & Diagnosis ==
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34 * (((
35 **Predictive Modeling:**
36
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.
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40 * (((
41 **Training with Multi-Modal Data:**
42
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.
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46 * (((
47 **Diagnostic Annotation System:**
48
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.
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56
57 ----
58
59 == 3. EEG, Neuroimaging & Sleep Analysis ==
60
61 * (((
62 **EEG/MEG Analysis:**
63
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:**
69
70 * Integrate sleep data from wearables (smartwatches, headbands) as early biomarkers.
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72 * (((
73 **Neuroimaging Analysis:**
74
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.
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78
79 ----
80
81 == 4. Clinical Validation & Pilot Testing ==
82
83 * (((
84 **Pilot Study Design:**
85
86 * Design a multicenter pilot study to validate AI-generated diagnostic scores.
87 * Recruit a clinical validation cohort from European research hospitals.
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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
98 * Implement clinician feedback loops to refine the AI model based on real-world usage.
99 * Publish validation results in peer-reviewed journals to enhance credibility.
100 )))
101
102 ----
103
104 == 5. Ethical, Regulatory & GDPR Compliance ==
105
106 * (((
107 **Regulatory Compliance:**
108
109 * Ensure all AI models comply with relevant regulations (e.g., EU AI Act, GDPR).
110 )))
111 * (((
112 **Privacy Preservation:**
113
114 * Implement privacy-preserving techniques (Federated Learning, Differential Privacy) to protect patient data.
115 * Develop data anonymization pipelines prior to AI processing.
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117 * (((
118 **Consent & Data Governance:**
119
120 * Establish consent management systems for patient data contributions.
121 * Ensure interoperability with hospital Electronic Health Record (EHR) systems.
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123
124 ----
125
126 == 6. EBRAINS Deployment & Cloud Infrastructure ==
127
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.
135
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
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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).
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167
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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>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
189 * **EBRAINS Collaboratory:** [[Neurodiagnoses on EBRAINS>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]