Summary

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

Details

Page properties

Content

...	...	@@ -76,3 +76,73 @@
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.