Wiki source code of Methodology

Version 6.1 by manuelmenendez on 2025/02/01 11:57

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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. Data Integration ===
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	9	==== Data Sources ====
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6.1	11	Biomedical Ontologies & Databases:
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6.1	13	* Human Phenotype Ontology (HPO) for symptom annotation.
	14	* Gene Ontology (GO) for molecular and cellular processes.
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6.1	16	Dimensionality Reduction and Interpretability:
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6.1	18	* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	19	* Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
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6.1	21	Neuroimaging & EEG/MEG Data:
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	23	* MRI volumetric measures for brain atrophy tracking.
	24	* EEG functional connectivity patterns (AI-Mind).
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	26	Clinical & Biomarker Data:
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	28	* CSF biomarkers (Amyloid-beta, Tau, Neurofilament Light).
	29	* Sleep monitoring and actigraphy data (ADIS).
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	31	Federated Learning Integration:
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	33	* Secure multi-center data harmonization (PROMINENT).
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6.1	37	==== Annotation System for Multi-Modal Data ====
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	39	To ensure structured integration of diverse datasets, Neurodiagnoses will implement an AI-driven annotation system, which will:
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	41	* Assign standardized metadata tags to diagnostic features.
	42	* Provide contextual explanations for AI-based classifications.
	43	* Track temporal disease progression annotations to identify long-term trends.
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1.1	47	=== 2. AI-Based Analysis ===
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6.1	49	==== Machine Learning & Deep Learning Models ====
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6.1	51	Risk Prediction Models:
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6.1	53	* LETHE’s cognitive risk prediction model integrated into the annotation framework.
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6.1	55	Biomarker Classification & Probabilistic Imputation:
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6.1	57	* KNN Imputer and Bayesian models used for handling missing biomarker data.
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	59	Neuroimaging Feature Extraction:
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	61	* MRI & EEG data annotated with neuroanatomical feature labels.
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	63	==== AI-Powered Annotation System ====
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	65	* Uses SHAP-based interpretability tools to explain model decisions.
	66	* Generates automated clinical annotations in structured reports.
	67	* Links findings to standardized medical ontologies (e.g., SNOMED, HPO).
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6.1	71	=== 3. Diagnostic Framework & Clinical Decision Support ===
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6.1	73	==== Tridimensional Diagnostic Axes ====
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6.1	75	Axis 1: Etiology (Pathogenic Mechanisms)
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6.1	77	* Classification based on genetic markers, cellular pathways, and environmental risk factors.
	78	* AI-assisted annotation provides causal interpretations for clinical use.
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6.1	80	Axis 2: Molecular Markers & Biomarkers
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6.1	82	* Integration of CSF, blood, and neuroimaging biomarkers.
	83	* Structured annotation highlights biological pathways linked to diagnosis.
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6.1	85	Axis 3: Neuroanatomoclinical Correlations
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	87	* MRI and EEG data provide anatomical and functional insights.
	88	* AI-generated progression maps annotate brain structure-function relationships.
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6.1	92	=== 4. Computational Workflow & Annotation Pipelines ===
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6.1	94	==== Data Processing Steps ====
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6.1	96	Data Ingestion:
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	98	* Harmonized datasets stored in EBRAINS Bucket.
	99	* Preprocessing pipelines clean and standardize data.
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	101	Feature Engineering:
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	103	* AI models extract clinically relevant patterns from EEG, MRI, and biomarkers.
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	105	AI-Generated Annotations:
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	107	* Automated tagging of diagnostic features in structured reports.
	108	* Explainability modules (SHAP, LIME) ensure transparency in predictions.
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	110	Clinical Decision Support Integration:
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	112	* AI-annotated findings fed into interactive dashboards.
	113	* Clinicians can adjust, validate, and modify annotations.
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6.1	117	=== 5. Validation & Real-World Testing ===
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6.1	119	==== Prospective Clinical Study ====
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6.1	121	* Multi-center validation of AI-based annotations & risk stratifications.
	122	* Benchmarking against clinician-based diagnoses.
	123	* Real-world testing of AI-powered structured reporting.
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6.1	125	==== Quality Assurance & Explainability ====
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6.1	127	* Annotations linked to structured knowledge graphs for improved transparency.
	128	* Interactive annotation editor allows clinicians to validate AI outputs.
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	132	=== 6. Collaborative Development ===
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6.1	134	The project is open to contributions from researchers, clinicians, and developers.
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6.1	136	Key tools include:
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1.1	138	* Jupyter Notebooks: For data analysis and pipeline development.
6.1	139	Example: probabilistic imputation**
1.1	140	* Wiki Pages: For documenting methods and results.
	141	* Drive and Bucket: For sharing code, data, and outputs.
6.1	142	* Collaboration with related projects:
	143	Example: Beyond the hype: AI in dementia – from early risk detection to disease treatment**
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	147	=== 7. Tools and Technologies ===
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6.1	149	==== Programming Languages: ====
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	151	* Python for AI and data processing.
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	153	==== Frameworks: ====
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	155	* TensorFlow and PyTorch for machine learning.
	156	* Flask or FastAPI for backend services.
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	158	==== Visualization: ====
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	160	* Plotly and Matplotlib for interactive and static visualizations.
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	162	==== EBRAINS Services: ====
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	164	* Collaboratory Lab for running Notebooks.
	165	* Buckets for storing large datasets.
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	169	=== Why This Matters ===
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	171	* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
	172	* It enables real-time tracking of disease progression across the three diagnostic axes.
	173	* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.

Wiki source code of Methodology

Neurodiagnoses