Wiki source code of to-do-list

Version 2.1 by manuelmenendez on 2025/01/29 18:43
Show last authors
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.
79
80 == **2. AI-Based Risk Prediction & Diagnosis** ==
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.
91
92 == **3. EEG, Neuroimaging & Sleep Analysis** ==
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.
99
100 == **4. Clinical Validation & Pilot Testing** ==
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.
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.
109
110 == **5. Ethical, Regulatory & GDPR Compliance** ==
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).
118
119 == **6. EBRAINS Deployment & Cloud Infrastructure** ==
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.
125
126 == **7. Interactive Web App for Clinicians & Researchers** ==
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.
133
134 == **8. Cross-Project Collaborations** ==
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.
141
142 == **9. Long-Term Expansion & Future Goals** ==
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.