Changes for page to-do-list

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

From 2.1 to 1.3 From 4.1 to 3.1

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edited by manuelmenendez
on 2025/02/05 13:28

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edited by manuelmenendez
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--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. Data Management & Integration** ==
------
++* Upload and organize harmonized biomarker datasets (PROMINENT).
++* Store EEG, sleep, and neuroimaging data in the EBRAINS Bucket.
++* Convert all datasets to .csv, .json, or .h5 formats for easier AI processing.
++* Implement automated data ingestion scripts to streamline updates from new sources.
++* Set up data harmonization methods to ensure consistency across different sources.
++* Enable Federated Learning to train AI models on multi-center data without sharing raw patient data (GDPR-compliant).
--== 1. Data Management & Integration ==
++== **2. AI-Based Risk Prediction & Diagnosis** ==
--* (((
--**Data Acquisition & Storage:**
++* Implement machine learning models for dementia risk stratification (LETHE).
++* Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
++* Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
++* Store pretrained models in the /models/ directory for future use.
++* Implement real-time AI-based diagnostic annotation for clinicians.
++* Add confidence intervals to AI predictions for clinical decision support.
++* Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
++* Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
++* Explore the use of Large Language Models (LLMs) for medical report summarization.
--* Download raw data from external sources (e.g., ADNI, GP2, PPMI, Enroll-HD, UK Biobank, etc.).
--* Upload and organize datasets in EBRAINS Buckets and in the /datasets/ directory on GitHub.
--)))
--* (((
--**Data Conversion & Format:**
++== **3. EEG, Neuroimaging & Sleep Analysis** ==
--* Convert all datasets to standardized formats (.csv, .json, .h5) to facilitate AI processing.
--)))
--* (((
--**Data Harmonization:**
++* Process EEG/MEG data using MNE-Python (AI-Mind).
++* Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
++* Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
++* Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
++* Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.
--* Implement automated data ingestion scripts to streamline updates from new sources.
--* Set up data harmonization methods to ensure consistency across different sources (e.g., genetics, neuroimaging, biomarkers, digital health).
--)))
--* (((
--**Federated Learning:**
++== **4. Clinical Validation & Pilot Testing** ==
--* Enable federated learning techniques to train AI models on multi-center data without sharing raw patient data (ensuring GDPR compliance).
--)))
++* Design a pilot study to validate AI-generated diagnostic scores.
++* Recruit a multicenter clinical validation cohort across European research hospitals.
++* Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
++* Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
++* Conduct a prospective study to test predictive accuracy over time.
++* Implement clinician feedback loops to refine the AI model based on real-world usage.
++* Publish validation results in peer-reviewed journals for credibility.
------
++== **5. Ethical, Regulatory & GDPR Compliance** ==
--== 2. AI-Based Risk Prediction & Diagnosis ==
++* Ensure AI models comply with the EU AI Act for medical applications.
++* Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
++* Develop patient data anonymization pipelines before AI processing.
++* Secure ethics approval for data usage in clinical applications.
++* Set up consent management systems for patient data contributions.
++* Ensure interoperability with hospital Electronic Health Records (EHRs).
--* (((
--**Predictive Modeling:**
++== **6. EBRAINS Deployment & Cloud Infrastructure** ==
--* Implement machine learning models (e.g., Random Forest, Neural Networks) for dementia risk stratification.
--* Develop probabilistic models (e.g., KNN Imputer, Bayesian approaches) to handle missing data.
--)))
--* (((
--**Training with Multi-Modal Data:**
++* Deploy AI models on EBRAINS Cloud for real-time inference.
++* Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
++* Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
++* Optimize computational efficiency to ensure AI inference runs on real-time clinical data.
--* Train AI models using data from biomarkers, EEG, MRI, and lifestyle factors.
--* Store pre-trained models in the /models/ directory for future use.
--)))
--* (((
--**Diagnostic Annotation System:**
++== **7. Interactive Web App for Clinicians & Researchers** ==
--* Implement real-time AI-based diagnostic annotation that produces two types of reports for each case:
--** **Probabilistic Diagnosis:** Traditional diagnosis with associated probability percentages.
--** **Tridimensional Diagnosis:** A structured classification based on three axes—etiology, molecular markers, and neuroanatomoclinical correlations.
--* Integrate Explainable AI techniques (e.g., SHAP) to ensure transparency in predictions.
--* Explore advanced deep learning methods for pattern recognition in neuroimaging data.
--* Investigate the use of Large Language Models (LLMs) for summarizing and generating medical reports.
--)))
++* Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
++* Allow clinicians to input biomarker data and get real-time AI predictions.
++* Enable PDF report generation for clinical decision-making.
++* Integrate custom dashboards with risk stratification results.
++* Deploy the tool on **neurodiagnoses.com** using Netlify, Vercel, or AWS.
------
++== **8. Cross-Project Collaborations** ==
--== 3. EEG, Neuroimaging & Sleep Analysis ==
++* Partner with AI-Mind for integrating EEG-based predictive models.
++* Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
++* Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
++* Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
++* Expand partnerships with clinical institutions to increase dataset size.
--* (((
--**EEG/MEG Analysis:**
++== **9. Long-Term Expansion & Future Goals** ==
--* Process EEG/MEG data using tools like MNE-Python.
--* Apply spectral analysis and connectivity metrics to derive EEG biomarkers for dementia detection.
--)))
--* (((
--**Sleep Monitoring:**
++* Explore AI-powered disease progression models for tracking neurodegeneration over time.
++* Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
++* Investigate genomics and proteomics for precision diagnostics.
++* Integrate wearable health tracking for continuous cognitive assessment.
++* Create an open-access AI diagnostic API for global research collaborations.
--* Integrate sleep data from wearables (smartwatches, headbands) as early biomarkers.
--)))
--* (((
--**Neuroimaging Analysis:**
++== **2. AI-Based Risk Prediction & Diagnosis** ==
--* Utilize MRI volumetric analysis to assess brain atrophy in high-risk patients.
--* Implement functional MRI (fMRI) analysis to correlate neuroanatomical changes with cognitive function.
--)))
++* Implement machine learning models for dementia risk stratification (LETHE).
++* Develop AI-based probabilistic models for filling in missing data (KNN Imputer, Bayesian approaches).
++* Train AI models using multi-modal data (biomarkers, EEG, MRI, and lifestyle factors).
++* Store pretrained models in the /models/ directory for future use.
++* Implement real-time AI-based diagnostic annotation for clinicians.
++* Add confidence intervals to AI predictions for clinical decision support.
++* Integrate Explainable AI (SHAP analysis) to ensure transparency and clinician trust.
++* Implement deep learning for pattern recognition in neuroimaging data (MRI/PET-based feature extraction).
++* Explore the use of Large Language Models (LLMs) for medical report summarization.
------
++== **3. EEG, Neuroimaging & Sleep Analysis** ==
--== 4. Clinical Validation & Pilot Testing ==
++* Process EEG/MEG data using MNE-Python (AI-Mind).
++* Apply EEG biomarkers for dementia detection (spectral analysis, connectivity metrics).
++* Integrate sleep monitoring data from ADIS (smartwatches, headbands) as an early biomarker.
++* Use MRI volumetric analysis for assessing brain atrophy in high-risk patients.
++* Implement functional MRI (fMRI) analysis to link neuroanatomical changes with cognitive function.
--* (((
--**Pilot Study Design:**
++== **4. Clinical Validation & Pilot Testing** ==
--* Design a multicenter pilot study to validate AI-generated diagnostic scores.
--* Recruit a clinical validation cohort from European research hospitals.
--)))
--* (((
--**Performance Evaluation:**
--
--* Compare AI-based diagnoses with traditional clinician diagnoses.
--* Develop and track validation metrics (e.g., AUROC, precision-recall, false positive rates).
--)))
--* (((
--**Feedback and Refinement:**
--
++* Design a pilot study to validate AI-generated diagnostic scores.
++* Recruit a multicenter clinical validation cohort across European research hospitals.
++* Compare AI-based diagnoses with clinician-based diagnoses to measure performance.
++* Develop validation metrics (e.g., AUROC, precision-recall, false positive rates).
++* Conduct a prospective study to test predictive accuracy over time.
  * Implement clinician feedback loops to refine the AI model based on real-world usage.
--* Publish validation results in peer-reviewed journals to enhance credibility.
--)))
++* Publish validation results in peer-reviewed journals for credibility.
------
++== **5. Ethical, Regulatory & GDPR Compliance** ==
--== 5. Ethical, Regulatory & GDPR Compliance ==
++* Ensure AI models comply with the EU AI Act for medical applications.
++* Implement privacy-preserving AI techniques (Federated Learning, Differential Privacy).
++* Develop patient data anonymization pipelines before AI processing.
++* Secure ethics approval for data usage in clinical applications.
++* Set up consent management systems for patient data contributions.
++* Ensure interoperability with hospital Electronic Health Records (EHRs).
--* (((
--**Regulatory Compliance:**
++== **6. EBRAINS Deployment & Cloud Infrastructure** ==
--* Ensure all AI models comply with relevant regulations (e.g., EU AI Act, GDPR).
--)))
--* (((
--**Privacy Preservation:**
++* Deploy AI models on EBRAINS Cloud for real-time inference.
++* Set up Jupyter Notebooks in EBRAINS Lab for collaborative development.
++* Automate model training pipelines using GitHub Actions or EBRAINS’ HPC.
++* Optimize computational efficiency to ensure AI inference runs on real-time clinical data.
--* Implement privacy-preserving techniques (Federated Learning, Differential Privacy) to protect patient data.
--* Develop data anonymization pipelines prior to AI processing.
--)))
--* (((
--**Consent & Data Governance:**
++== **7. Interactive Web App for Clinicians & Researchers** ==
--* Establish consent management systems for patient data contributions.
--* Ensure interoperability with hospital Electronic Health Record (EHR) systems.
--)))
++* Develop an interactive web-based AI diagnostic tool (Flask, FastAPI, or Streamlit).
++* Allow clinicians to input biomarker data and get real-time AI predictions.
++* Enable PDF report generation for clinical decision-making.
++* Integrate custom dashboards with risk stratification results.
++* Deploy the tool on **neurodiagnoses.com** using Netlify, Vercel, or AWS.
------
++== **8. Cross-Project Collaborations** ==
--== 6. EBRAINS Deployment & Cloud Infrastructure ==
++* Partner with AI-Mind for integrating EEG-based predictive models.
++* Collaborate with LETHE on lifestyle-based cognitive decline risk scoring.
++* Use PROMINENT’s multi-modal AI pipeline for refining dementia subtype classification.
++* Leverage ADIS sleep monitoring research for non-invasive biomarker expansion.
++* Expand partnerships with clinical institutions to increase dataset size.
--* **Cloud Deployment:**
--** Deploy AI models on the EBRAINS Cloud for real-time inference.
--* **Collaborative Development:**
--** Set up Jupyter Notebooks in EBRAINS Lab for collaborative development and testing.
--** Automate model training pipelines using GitHub Actions or EBRAINS HPC resources.
--* **Optimization:**
--** Optimize computational efficiency to enable real-time processing of clinical data.
++== **9. Long-Term Expansion & Future Goals** ==
------
--
--== 7. Interactive Web Application for Clinicians & Researchers ==
--
--* **Web App Development:**
--** Develop an interactive web-based diagnostic tool using frameworks such as Flask, FastAPI, or Streamlit.
--** Allow clinicians to input biomarker data and receive real-time AI predictions.
--* **Report Generation:**
--** Enable the generation of PDF reports for clinical decision support.
--* **Custom Dashboards:**
--** Integrate dashboards that display risk stratification results.
--* **Deployment:**
--** Deploy the web app on neurodiagnoses.com using hosting services like Netlify, Vercel, or AWS.
--
------
--
--== 8. Cross-Project Collaborations ==
--
--* (((
--**External Partnerships:**
--
--* Collaborate with projects such as AI-Mind for EEG-based predictive modeling.
--* Work with LETHE for lifestyle-based cognitive decline risk scoring.
--* Leverage PROMINENT’s multi-modal AI pipeline to refine dementia subtype classification.
--* Expand partnerships with clinical institutions to enhance dataset diversity.
--)))
--* (((
--**Open-Source Community:**
--
--* Encourage contributions via GitHub (code improvements, new features) and EBRAINS discussion pages (research and validation).
--)))
--
------
--
--== 9. Long-Term Expansion & Future Goals ==
--
--* **Disease Progression Modeling:**
--** Explore AI-powered models for tracking neurodegeneration over time.
--* **CNS Digital Twins:**
--** Develop CNS Digital Twins by integrating multi-omics data, neuroimaging, and digital health records to create personalized simulations of disease progression.
--* **Continuous Monitoring:**
--** Investigate the integration of wearable health tracking devices for ongoing cognitive assessment.
--* **Open-Access API:**
--** Create an API to allow global research collaborations with access to AI diagnostic tools.
--* **Sustainability & Updates:**
--** Regularly update the system with new data and algorithm improvements.
--** Establish long-term funding and partnership strategies to ensure sustainability.
--
------
--
--=== Key Resources ===
--
--* **GitHub Repository:** [[Neurodiagnoses on GitHub>>url:https://github.com/manuelmenendezgonzalez/neurodiagnoses]]
--* **EBRAINS Collaboratory:** [[Neurodiagnoses on EBRAINS>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
++* Explore AI-powered disease progression models for tracking neurodegeneration over time.
++* Develop real-time multimodal patient monitoring (EEG, MRI, biomarkers, lifestyle).
++* Investigate genomics and proteomics for precision diagnostics.
++* Integrate wearable health tracking for continuous cognitive assessment.
++* Create an open-access AI diagnostic API for global research collaborations.