Wiki source code of Neurodiagnoses

Version 41.1 by manuelmenendez on 2025/02/02 15:13

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40.1	5	= //A new tridimensional diagnostic framework for complex CNS diseases// =
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40.1	7	This project is focused on developing a novel nosological and diagnostic framework for complex CNS diseases by using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies.
6.1	8	We aim to create a structured, interpretable, and scalable diagnostic tool.
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6.1	16	= What is this about and what can I find here? =
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27.1	18	= Overview =
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40.1	21	The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional phenotype-based approaches, which often fail to capture the complex pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes that drive disease progression.
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40.1	23	Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex CNS disorders, including neurodegenerative diseases, autoimmune encephalopathies, prion disorders, and genetic syndromes. The project aims to develop a tridimensional diagnostic framework with an AI-powered annotation system, integrating etiology, molecular biomarkers, and neuroanatomoclinical correlations for precise, standardized, and scalable CNS disease diagnostics.
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23.1	25	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
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16.1	27	* Axis 1: Etiology (genetic or other causes of diseases).
	28	* Axis 2: Molecular Markers (biomarkers).
6.1	29	* Axis 3: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
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	31	This methodology enables:
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	33	* Greater precision in diagnosis.
	34	* Integration of incomplete datasets using AI-driven probabilistic modeling.
	35	* Stratification of patients for personalized treatment.
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41.1	37	== The role of AI-powered annotation ==
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36.1	39	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
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36.1	41	* Assigns structured metadata tags to diagnostic features.
	42	* Provides real-time contextual explanations for AI-based classifications.
	43	* Tracks longitudinal disease progression using timestamped AI annotations.
	44	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
	45	* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
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37.1	47	Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
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	49	1. Traditional Probabilistic Diagnosis
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	51	* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
	52	* Example Output:
39.1	53	** 75% Alzheimer's Disease
	54	** 20% Lewy Body Dementia
	55	** 5% Vascular Dementia
37.1	56	* Useful for differential diagnosis and treatment decision-making.
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	58	2. Tridimensional Diagnosis
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	60	* Diagnoses are structured based on:
	61	(1) Etiology (genetic, autoimmune, metabolic, infectious)
	62	(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
	63	(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
39.1	64	* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time.
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	66	For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
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23.1	69	== The case of neurodegenerative diseases ==
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23.1	71	There have been described these 3 diagnostic axes:
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25.1	73	[[Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.>>image:tridimensional.png\|\|alt="tridimensional view of neurodegenerative diseases"]]
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6.1	75	* (((
	76	Axis 1: Etiology
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	78	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
	79	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
23.1	80	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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	84	* (((
	85	Axis 2: Molecular Markers
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	87	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
	88	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
	89	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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	93	* (((
	94	Axis 3: Neuroanatomoclinical
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	96	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
	97	* //Examples//: Hippocampal atrophy correlating with memory deficits.
	98	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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9.1	101	== Applications ==
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	103	This system enhances:
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7.2	105	* Research: By stratifying patients, reduces cohort heterogeneity in clinical trials.
6.1	106	* Clinical Practice: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
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10.1	108	== How to Contribute ==
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	110	* Access the `/docs` folder for guidelines.
	111	* Use `/code` for the latest AI pipelines.
	112	* Share feedback and ideas in the wiki discussion pages.
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10.1	114	== Key Objectives ==
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	116	* Develop interpretable AI models for diagnosis and progression tracking.
	117	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
	118	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
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	120	== Who has access? ==
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	122	We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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	132	{{box title="Contents"}}
	133	{{toc/}}
	134	{{/box}}
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10.1	136	== Main contents ==
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5.1	138	* `/docs`: Documentation and contribution guidelines.
	139	* `/code`: Machine learning pipelines and scripts.
	140	* `/data`: Sample datasets for testing.
	141	* `/outputs`: Generated models, visualizations, and reports.
21.1	142	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
35.1	143	* [[Notebooks>>Notebooks]]
21.1	144	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
22.1	145	* [[to-do-list>>to-do-list]]
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