Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -76,73 +76,3 @@
76	76	* Investigate genomics and proteomics for precision diagnostics.
77	77	* Integrate wearable health tracking for continuous cognitive assessment.
78	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.