Wiki source code of Methodology

Version 8.1 by manuelmenendez on 2025/02/01 14:12

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6.1	1	==== Overview ====
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6.1	3	This project develops a tridimensional diagnostic framework for CNS diseases, incorporating AI-powered annotation tools to improve interpretability, standardization, and clinical utility. The methodology integrates multi-modal data, including genetic, neuroimaging, neurophysiological, and biomarker datasets, and applies machine learning models to generate structured, explainable diagnostic outputs.
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7.1	5	=== Workflow ===
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	7	1. (((
8.1	8	We Use [[https:~~/~~/github.com/users/manuelmenendezgonzalez/projects/1>>https://GitHub for AI Development]]
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	10	* Create a GitHub repository for AI scripts and models.
	11	* Use GitHub Projects to manage research milestones.
	12	)))
	13	1. (((
	14	We Use EBRAINS for Data & Collaboration
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	16	* Store biomarker and neuroimaging data in EBRAINS Buckets.
	17	* Run Jupyter Notebooks in EBRAINS Lab to test AI models.
	18	* Use EBRAINS Wiki for structured documentation and research discussion.
	19	)))
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1.1	21	----
	22
	23	=== 1. Data Integration ===
	24
	25	==== Data Sources ====
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6.1	27	Biomedical Ontologies & Databases:
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6.1	29	* Human Phenotype Ontology (HPO) for symptom annotation.
	30	* Gene Ontology (GO) for molecular and cellular processes.
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6.1	32	Dimensionality Reduction and Interpretability:
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6.1	34	* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	35	* Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
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6.1	37	Neuroimaging & EEG/MEG Data:
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	39	* MRI volumetric measures for brain atrophy tracking.
	40	* EEG functional connectivity patterns (AI-Mind).
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	42	Clinical & Biomarker Data:
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	44	* CSF biomarkers (Amyloid-beta, Tau, Neurofilament Light).
	45	* Sleep monitoring and actigraphy data (ADIS).
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	47	Federated Learning Integration:
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	49	* Secure multi-center data harmonization (PROMINENT).
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6.1	53	==== Annotation System for Multi-Modal Data ====
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	55	To ensure structured integration of diverse datasets, Neurodiagnoses will implement an AI-driven annotation system, which will:
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	57	* Assign standardized metadata tags to diagnostic features.
	58	* Provide contextual explanations for AI-based classifications.
	59	* Track temporal disease progression annotations to identify long-term trends.
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	61	----
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1.1	63	=== 2. AI-Based Analysis ===
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6.1	65	==== Machine Learning & Deep Learning Models ====
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6.1	67	Risk Prediction Models:
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6.1	69	* LETHE’s cognitive risk prediction model integrated into the annotation framework.
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6.1	71	Biomarker Classification & Probabilistic Imputation:
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6.1	73	* KNN Imputer and Bayesian models used for handling missing biomarker data.
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	75	Neuroimaging Feature Extraction:
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	77	* MRI & EEG data annotated with neuroanatomical feature labels.
	78
	79	==== AI-Powered Annotation System ====
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	81	* Uses SHAP-based interpretability tools to explain model decisions.
	82	* Generates automated clinical annotations in structured reports.
	83	* Links findings to standardized medical ontologies (e.g., SNOMED, HPO).
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1.1	85	----
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6.1	87	=== 3. Diagnostic Framework & Clinical Decision Support ===
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6.1	89	==== Tridimensional Diagnostic Axes ====
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6.1	91	Axis 1: Etiology (Pathogenic Mechanisms)
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6.1	93	* Classification based on genetic markers, cellular pathways, and environmental risk factors.
	94	* AI-assisted annotation provides causal interpretations for clinical use.
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6.1	96	Axis 2: Molecular Markers & Biomarkers
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6.1	98	* Integration of CSF, blood, and neuroimaging biomarkers.
	99	* Structured annotation highlights biological pathways linked to diagnosis.
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6.1	101	Axis 3: Neuroanatomoclinical Correlations
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	103	* MRI and EEG data provide anatomical and functional insights.
	104	* AI-generated progression maps annotate brain structure-function relationships.
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1.1	106	----
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6.1	108	=== 4. Computational Workflow & Annotation Pipelines ===
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6.1	110	==== Data Processing Steps ====
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6.1	112	Data Ingestion:
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	114	* Harmonized datasets stored in EBRAINS Bucket.
	115	* Preprocessing pipelines clean and standardize data.
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	117	Feature Engineering:
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	119	* AI models extract clinically relevant patterns from EEG, MRI, and biomarkers.
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	121	AI-Generated Annotations:
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	123	* Automated tagging of diagnostic features in structured reports.
	124	* Explainability modules (SHAP, LIME) ensure transparency in predictions.
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	126	Clinical Decision Support Integration:
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	128	* AI-annotated findings fed into interactive dashboards.
	129	* Clinicians can adjust, validate, and modify annotations.
	130
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6.1	133	=== 5. Validation & Real-World Testing ===
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6.1	135	==== Prospective Clinical Study ====
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6.1	137	* Multi-center validation of AI-based annotations & risk stratifications.
	138	* Benchmarking against clinician-based diagnoses.
	139	* Real-world testing of AI-powered structured reporting.
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6.1	141	==== Quality Assurance & Explainability ====
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6.1	143	* Annotations linked to structured knowledge graphs for improved transparency.
	144	* Interactive annotation editor allows clinicians to validate AI outputs.
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	146	----
	147
	148	=== 6. Collaborative Development ===
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6.1	150	The project is open to contributions from researchers, clinicians, and developers.
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6.1	152	Key tools include:
	153
1.1	154	* Jupyter Notebooks: For data analysis and pipeline development.
6.1	155	Example: probabilistic imputation**
1.1	156	* Wiki Pages: For documenting methods and results.
	157	* Drive and Bucket: For sharing code, data, and outputs.
6.1	158	* Collaboration with related projects:
	159	Example: Beyond the hype: AI in dementia – from early risk detection to disease treatment**
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	161	----
	162
	163	=== 7. Tools and Technologies ===
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6.1	165	==== Programming Languages: ====
	166
	167	* Python for AI and data processing.
	168
	169	==== Frameworks: ====
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	171	* TensorFlow and PyTorch for machine learning.
	172	* Flask or FastAPI for backend services.
	173
	174	==== Visualization: ====
	175
	176	* Plotly and Matplotlib for interactive and static visualizations.
	177
	178	==== EBRAINS Services: ====
	179
	180	* Collaboratory Lab for running Notebooks.
	181	* Buckets for storing large datasets.
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	183	----
	184
	185	=== Why This Matters ===
	186
	187	* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
	188	* It enables real-time tracking of disease progression across the three diagnostic axes.
	189	* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.

Wiki source code of Methodology

Neurodiagnoses